Leading Edge Review Cell Timescales of Genetic and Epigenetic Inheritance Oliver J Rando and Kevin J. Verstrepen epartment of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605 USA 2FAS Center for Systems Biology, Harvard University, 7 Divinity Avenue, Cambridge, MA 02138, USA ment of Molecular and Microbial Systems, K.U. Leuven, Faculty of Applied Bioscience and Engineering Kasteelpark Arenberg 22, B-3001 Leuven (Heverlee), Belgium dence: oliver. rando@umassmed edu(oJ. R), kverstrepen@cgr. harvard. edu(KJ v) DO10.1016/ce.2007.01.023 According to classical evolutionary theory, phenotypic variation originates from random mu- tations that are independent of selective pressure. However, recent findings suggest that organisms have evolved mechanisms to influence the timing or genomic location of herta ble variability. Hypervariable contingency loci and epigenetic switches increase the variabl ity of specific phenotypes; error-prone DNA replicases produce bursts of variability in times of stress. Interestingly, these mechanisms seem to tune the variability of a given phenotype to match the variability of the acting selective pressure. Although these observations do not undermine Darwin's theory, they suggest that selection and variability are less independent than once thought 1942). By contrast, other phenotypes exhibit unusually In 1943, by plating a number of independent bacterial rapid variation due to underlying hypervariable sequences cultures onto lawns of infectious phages, Salvador Luria in the genome(Srikhanta et aL, 2005; van der Woude and er phenotypes exhibit rapid varia- contained a widely variable number of phage-resistant on despite no underlying genotypic change; these pheno- mutants(Luria and Delbruck, 1943). Hence, they argued, types belong to the class of " epigenetically"heritable these mutants must have been generated prior to the phenotypes (for a review, see Jablonka and Lamb, 1995) phage infection and not in response to the infection, These and many other examples demonstrate that pheno- that would likely produce a comparable number of mu- typic stability spans many orders of magnitude beyond the tants in each culture. The apparent independence of va range expected from classic genetic mutation studies, iation and selection confirmed a comerstone of the classic with some phenotypes varying rapidly while others are Neo-Darwinist theory of evolution. In contrast to Darwin's unusually stable(Figure 1) original theory, the Neo-Darwinist theory firmly rejects Like phenotypic changes, changes in the selective pres Lamarck's idea that organisms pass on characteristics ure acting upon organisms also occur over an exception hey develop during their lives (Weismann, 1893).The ally broad timescale. Some changes, such as temperature Neo-Darwinian idea that evolution is driven by purely ran- changes and periods of famine, may occur within an dom germline mutations followed by independent natural organism s life span (one generation). Geological changes, selection on the progeny has become a widely accepted on the other hand, span several thousands or even millions dogma in biology. of biological generations. The ability of organisms to The resulting focus on mutation as the mechanism for change phenotypes to cope with changing environments henotypic variation has led to detailed during their lifetime is known as"plasticity. " For geological mutation rates. In addition, genotype-to-phenotype map- timescales, phenotypic change mostly occurs by se ping became one of the major focuses of the molecular quence evolution, and the ability to effect this change is biology revolution. Many studies have defined the stability called"evolvability. However, environments(and thus which is generally measured as the rate of change of the selection) change over timescales intermediate to these genotype per cellular generation, of various phenotypes. two. For example, predator-prey cycles, cyclical climate Notably, this massive research effort has identified pheno- changes such as El Nino, and battles between infectious types whose stability differs significantly from typical phe microbes and their host s immune system may all act on typic stabilities (Figure 1). For example, certain pheno- timescales greater than one generation but shorter than types are inherently less sensitive to mutation, and this geological timescales of thousands of generations sensitivity of a phenotype to genetic mutation is often re- This Review addresses the timescales, over which her- ferred to as"robustness "or"canalization"(addington itable biological phenotypes vary, and gathers examples ce128,655668. February23,2007@2007 Elsevier Inc.655
Leading Edge Review Timescales of Genetic and Epigenetic Inheritance Oliver J. Rando1, * and Kevin J. Verstrepen2,3, * 1Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA 2FAS Center for Systems Biology, Harvard University, 7 Divinity Avenue, Cambridge, MA 02138, USA 3Department of Molecular and Microbial Systems, K.U.Leuven, Faculty of Applied Bioscience and Engineering, Kasteelpark Arenberg 22, B-3001 Leuven (Heverlee), Belgium *Correspondence: oliver.rando@umassmed.edu (O.J.R.), kverstrepen@cgr.harvard.edu (K.J.V.) DOI 10.1016/j.cell.2007.01.023 According to classical evolutionary theory, phenotypic variation originates from random mutations that are independent of selective pressure. However, recent findings suggest that organisms have evolved mechanisms to influence the timing or genomic location of heritable variability. Hypervariable contingency loci and epigenetic switches increase the variability of specific phenotypes; error-prone DNA replicases produce bursts of variability in times of stress. Interestingly, these mechanisms seem to tune the variability of a given phenotype to match the variability of the acting selective pressure. Although these observations do not undermine Darwin’s theory, they suggest that selection and variability are less independent than once thought. Introduction In 1943, by plating a number of independent bacterial cultures onto lawns of infectious phages, Salvador Luria and Max Delbru¨ ck showed that each bacterial population contained a widely variable number of phage-resistant mutants (Luria and Delbru¨ ck, 1943). Hence, they argued, these mutants must have been generated prior to the phage infection and not in response to the infection, as that would likely produce a comparable number of mutants in each culture. The apparent independence of variation and selection confirmed a cornerstone of the classic Neo-Darwinist theory of evolution. In contrast to Darwin’s original theory, the Neo-Darwinist theory firmly rejects Lamarck’s idea that organisms pass on characteristics they develop during their lives (Weismann, 1893). The Neo-Darwinian idea that evolution is driven by purely random germline mutations followed by independent natural selection on the progeny has become a widely accepted dogma in biology. The resulting focus on mutation as the mechanism for phenotypic variation has led to detailed measurements of mutation rates. In addition, genotype-to-phenotype mapping became one of the major focuses of the molecular biology revolution. Many studies have defined the stability, which is generally measured as the rate of change of the phenotype per cellular generation, of various phenotypes. Notably, this massive research effort has identified phenotypes whose stability differs significantly from typical phenotypic stabilities (Figure 1). For example, certain phenotypes are inherently less sensitive to mutation, and this insensitivity of a phenotype to genetic mutation is often referred to as ‘‘robustness’’ or ‘‘canalization’’ (Waddington, 1942). By contrast, other phenotypes exhibit unusually rapid variation due to underlying hypervariable sequences in the genome (Srikhanta et al., 2005; van der Woude and Baumler, 2004). Still other phenotypes exhibit rapid variation despite no underlying genotypic change; these phenotypes belong to the class of ‘‘epigenetically’’ heritable phenotypes (for a review, see Jablonka and Lamb, 1995). These and many other examples demonstrate that phenotypic stability spans many orders of magnitude beyond the range expected from classic genetic mutation studies, with some phenotypes varying rapidly while others are unusually stable (Figure 1). Like phenotypic changes, changes in the selective pressure acting upon organisms also occur over an exceptionally broad timescale. Some changes, such as temperature changes and periods of famine, may occur within an organism’s life span (one generation). Geological changes, on the other hand, span several thousands or even millions of biological generations. The ability of organisms to change phenotypes to cope with changing environments during their lifetime is known as ‘‘plasticity.’’ For geological timescales, phenotypic change mostly occurs by sequence evolution, and the ability to effect this change is called ‘‘evolvability.’’ However, environments (and thus selection) change over timescales intermediate to these two. For example, predator-prey cycles, cyclical climate changes such as El Nin˜ o, and battles between infectious microbes and their host’s immune system may all act on timescales greater than one generation but shorter than geological timescales of thousands of generations. This Review addresses the timescales, over which heritable biological phenotypes vary, and gathers examples Cell 128, 655–668, February 23, 2007 ª2007 Elsevier Inc. 655
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 Ind
of biological mechanisms that are seemingly designed to regulate or at least influence the timing or location of phenotypic variation. Where possible, we will explore the correlation 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 organisms 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 recognized by the immune system and which switch between different expression states as rapidly as every 50 generations. 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 enabled 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 discussed, detailed studies of selective pressure in ecologically 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 genetic or epigenetic mechanisms, it is useful to first consider 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 complicated by the fact that mutation rates vary across the genome. 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 understood. 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 phenotypes. 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. Conversely, 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 sequence; it obviously depends very strongly on which proteins 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 advances in computational protein-structure prediction will enable some intuition concerning target size for the misfolding of arbitrary proteins, and functional genomic studies 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 decarboxylase (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 timescales, ranging from fewer than one cellular (or organismal) generation to more than one billion generations. A number of different mechanisms exist that regulate the stability of biological phenotypes. Phenotypes 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.
Cell Vm is defined as the per-generation increase in the math- Salmonella flagellar synthesis genes(Simon et al., 1980). ematical variance of a quantitative trait across a population The promoter is surrounded by inverted repeats, which due to random, unselected mutations. Mutational vari- are subject to frequent recombination events that result ance is typically measured by allowing a broad spectrum in promoter inversion. When the promoter inverts, the ex of unselected mutations to accumulate by passaging indi- pression of one flagellar gene is arrested, and a second tion sizes (eliminating any but the strongest effects of though the precise biological function of this phase varia- selection), followed by measurement of the phenotype tion remains emonstrated f two different flagellar antigens may help to evade the host im- Given a mutation rate and a target size, one may, in prin mune system and/or to infect different tissues(van der ple, predict the stability of a phenotype of interest. How- Woude and Baumler, 2004). Many other contingency loci ever, researchers have discovered several cellular mech- have been described, mostly in pathogenic microorgan- anisms that increase or decrease the rates of change a subset of phenotypes. It is useful here to distinguish be- pression of cell-surface antigens. A special case is that tween regulation of global variation, locus-specific varia- of the trypanosomes, which contain an arsenal of about tion, and temporal regulation of variation(local or global; 1000 silent"variant surface glycoproteins"(VSGs ). Only Jablonka and Lamb, 2005: Metzgar and Wills, 2000) the one gene localized in the active VSG expression site The broad idea that cells have evolved the ability to regu- is transcribed. By regularly replacing the VSG gene ate the global tempo of phenotypic change is irrefutable. the active expression site, the parasites constantly switch The existence of proofreading activities and sophisticated their outer surface coat(Barry and McCulloch, 2001) error-correction systems encoded in most genomes dem Another interesting case of contingency loci is found in onstrates that evolution has selected for systems that the common brewer's yeast Saccharomyces cerevisiae modulate the fidelity of information transfer between gen- Many S. cerevisiae cell-surface genes contain tandemly erations. Indeed, subpopulations of cells lacking proof repeated DNA sequences in their coding sequences reading activities known as "mutators, are found a (Verstrepen et aL., 2004, 2005). The repeats are subject high frequencies (often on the order of 1%)in microbes to frequent recombination events, which often result in gathered from the environment(LeClerc et al., 1996). repeats being gained or lost(Figure 2). One such gene, However, we aim specifically to discuss examples of FLO1, encodes a cell-surface protein that enables yeast localized variation in the fidelity of information transfer cells to adhere to various substrates. Cells carrying a (genotypic or, in some cases, exclusively phenotypic). greater number of repeats in FLo1 show a stronger adher- We will also discuss mechanisms that regulate the timing ence to plastic surfaces such as those used in medical de of variability, with cellular stress generally leading to in- vices. Repeat variation may therefore allow fungi to rapidly creased variation. Finally, we describe a few examples attune their cell surfaces to new environments. It is inter where cells are able to influence both the timing and loca- esting to note that in this case, the repeats do not ca tion of variability in response to environmental cues. switching of expression states in a repertoire of cell-sur- face genes. Instead, unstable intragenic repeats generate Localized variation limited changes in a small set of expressed proteins. Sim- Contingency Loci and Rapid Genotypic Variation ilar repeat variation in genes of pathogenic fungi may con- Analysis of mutation rates in the E. coli Lac operon tribute to the cell-surface variability needed to evade the showed that many mutation hot spots corresponded not host immune system (Verstrepen et al., 2005) to base substitutions but to insertions and deletions in Although they are usually not referred to as contingency short repeated sequences(Farabaugh et al., 1978). Since loci, similar hypervariable loci are also found in m then, numerous examples have been described of rapid zoans, including humans(where they are often associated sequence change associated with hypervariable DNa with diseases). Classic examples include neurodegenera- loci, termed"contingency loci"(for a review, see van der tive diseases, such as Huntington's chorea and fragile x Woude and Baumler, 2004). Through various mecha syndrome, where expansion of intragenic repeats leads nisms, these loci are unusually prone to specific types of to malfunction of the associated gene. The timescale of mutations that result in the altemating on- and off-switch these expansion/contraction events has been extensivel ing of specific genes. Switching between the two resulting studied in fragile X syndrome, where the rate of repeat ex- phenotypes(called"phase variation")enables organisms pansion varies depending on the sex of the carrier and the to quickly adapt to frequent and recurring changes in the initial(pre-existing) number of repeats: in females carrying environment. Switching frequencies as high as 10 alleles with 90-100 repeats, up to 87% of the offspring in- have been reported, although frequencies on the order herit a disease-causing full mutation (200 repeats). This of one switch in every 102-105 generations are more com- rate drops to m5% for the offspring of mothers carrying mon(van der Woude and Baumler, 2004) between 55 and 59 repeats, whereas mothers with fewer The best known examples of contingency loci are in than 55 repeats never pass on the full mutation to their bacteria. The term"contingency locus"was first coined children(Nolin et al., 2003). Interestingly, at many of these to describe the reversible promoter that controls the repeat-containing genes, repetition is highly conserved ce128,655668. February23,2007@2007 Elsevier Inc.657
Vm is defined as the per-generation increase in the mathematical variance of a quantitative trait across a population due to random, unselected mutations. Mutational variance is typically measured by allowing a broad spectrum of unselected mutations to accumulate by passaging individuals of a species independently at very small population sizes (eliminating any but the strongest effects of selection), followed by measurement of the phenotype of interest. Given a mutation rate and a target size, one may, in principle, predict the stability of a phenotype of interest. However, researchers have discovered several cellular mechanisms that increase or decrease the rates of change of a subset of phenotypes. It is useful here to distinguish between regulation of global variation, locus-specific variation, and temporal regulation of variation (local or global; Jablonka and Lamb, 2005; Metzgar and Wills, 2000). The broad idea that cells have evolved the ability to regulate the global tempo of phenotypic change is irrefutable. The existence of proofreading activities and sophisticated error-correction systems encoded in most genomes demonstrates that evolution has selected for systems that modulate the fidelity of information transfer between generations. Indeed, subpopulations of cells lacking proofreading activities, known as ‘‘mutators,’’ are found at high frequencies (often on the order of 1%) in microbes gathered from the environment (LeClerc et al., 1996). However, we aim specifically to discuss examples of localized variation in the fidelity of information transfer (genotypic or, in some cases, exclusively phenotypic). We will also discuss mechanisms that regulate the timing of variability, with cellular stress generally leading to increased variation. Finally, we describe a few examples where cells are able to influence both the timing and location of variability in response to environmental cues. Localized Variation Contingency Loci and Rapid Genotypic Variation Analysis of mutation rates in the E. coli Lac operon showed that many mutation hot spots corresponded not to base substitutions but to insertions and deletions in short repeated sequences (Farabaugh et al., 1978). Since then, numerous examples have been described of rapid sequence change associated with hypervariable DNA loci, termed ‘‘contingency loci’’ (for a review, see van der Woude and Baumler, 2004). Through various mechanisms, these loci are unusually prone to specific types of mutations that result in the alternating on- and off-switching of specific genes. Switching between the two resulting phenotypes (called ‘‘phase variation’’) enables organisms to quickly adapt to frequent and recurring changes in the environment. Switching frequencies as high as 101 have been reported, although frequencies on the order of one switch in every 103 –105 generations are more common (van der Woude and Baumler, 2004). The best known examples of contingency loci are in bacteria. The term ‘‘contingency locus’’ was first coined to describe the reversible promoter that controls the Salmonella flagellar synthesis genes (Simon et al., 1980). The promoter is surrounded by inverted repeats, which are subject to frequent recombination events that result in promoter inversion. When the promoter inverts, the expression of one flagellar gene is arrested, and a second gene on the other side of the promoter is activated. Although the precise biological function of this phase variation remains to be demonstrated, the expression of two different flagellar antigens may help to evade the host immune system and/or to infect different tissues (van der Woude and Baumler, 2004). Many other contingency loci have been described, mostly in pathogenic microorganisms, where hypervariable loci commonly control the expression of cell-surface antigens. A special case is that of the trypanosomes, which contain an arsenal of about 1000 silent ‘‘variant surface glycoproteins’’ (VSGs). Only the one gene localized in the active VSG expression site is transcribed. By regularly replacing the VSG gene in the active expression site, the parasites constantly switch their outer surface coat (Barry and McCulloch, 2001). Another interesting case of contingency loci is found in the common brewer’s yeast Saccharomyces cerevisiae. Many S. cerevisiae cell-surface genes contain tandemly repeated DNA sequences in their coding sequences (Verstrepen et al., 2004, 2005). The repeats are subject to frequent recombination events, which often result in repeats being gained or lost (Figure 2). One such gene, FLO1, encodes a cell-surface protein that enables yeast cells to adhere to various substrates. Cells carrying a greater number of repeats in FLO1 show a stronger adherence to plastic surfaces such as those used in medical devices. Repeat variation may therefore allow fungi to rapidly attune their cell surfaces to new environments. It is interesting to note that in this case, the repeats do not cause switching of expression states in a repertoire of cell-surface genes. Instead, unstable intragenic repeats generate limited changes in a small set of expressed proteins. Similar repeat variation in genes of pathogenic fungi may contribute to the cell-surface variability needed to evade the host immune system (Verstrepen et al., 2005). Although they are usually not referred to as contingency loci, similar hypervariable loci are also found in metazoans, including humans (where they are often associated with diseases). Classic examples include neurodegenerative diseases, such as Huntington’s chorea and fragile X syndrome, where expansion of intragenic repeats leads to malfunction of the associated gene. The timescale of these expansion/contraction events has been extensively studied in fragile X syndrome, where the rate of repeat expansion varies depending on the sex of the carrier and the initial (pre-existing) number of repeats: in females carrying alleles with 90–100 repeats, up to 87% of the offspring inherit a disease-causing full mutation (>200 repeats). This rate drops to 5% for the offspring of mothers carrying between 55 and 59 repeats, whereas mothers with fewer than 55 repeats never pass on the full mutation to their children (Nolin et al., 2003). Interestingly, at many of these repeat-containing genes, repetition is highly conserved Cell 128, 655–668, February 23, 2007 ª2007 Elsevier Inc. 657
Cel and males with more repeat copies in the Avpr1a promoter FLO1 show increased caretaking for their pups and increase pair bonding with partner females compared to individuals vith fewer repeats. This repeat variation could therefore allow for rapid evolution of behavioral traits that may be at-driven recombination of adaptive benefit in different environments. a second ex- ample of repeat-associated phenotypic plasticity that is seemingly not pathogenic was found by Fondon and Gar- ner(Fondon and Gamer, 2004). These authors demon- strate that repeat variability in the coding regions of the Alx-4(aristaless-like 4)and Runx-2(runt-related transcrip- tion factor) genes is associated with quantitative differ- ences in limb and skull morphology in dogs. Hence, these repeats may allow rapid evolution of morphological vari- ants on a conserved basic body plan that may provide an adaptive advantage as the selective environment changes Epigenetic Inheritance and Rapid Phenotype Another class of phenotypes vary at rates similar to, or often even higher than those typically generated by conti gency loci In most cases, this variation does not rely on mutations in the DNA sequence but rather relies on altena- tive, so-called"epigenetic"methods of inheritance. Like ontingency loci, epigenetically heritable traits typically exhibit a limited repertoire of phenotypes and interconvert (switch )more rapidly than do phenotypes that change by point mutation. Epigenetic switches can be grouped Figure 2. Recombination in Intragenic Repeats according to the mechanism of inheritance, as epigenet information is carried by substrates ranging from DNA ertain genes, such as the s cerevisiae FLo1 gene, contain tandem peats within their coding sequences. These repeats are highly unsta methylation pattens to the folding of prion proteins. ble and recombine at frequencies around 10- per(mitotic or meiotic) Methylation of dNa bases is one of the major mecha resulting in the net loss or gain If the repeat nisms of epigenetic inheritance and has been implicated nits are not a multiple of three nucleotides, recombination gives rise in phenotypic inheritance in unicellular organisms, in frameshifts, resulting in switching on and off of the gene. Most cell-state inheritance in multicellular organisms(during peats found within open reading frames, ho ree nucleotides long. In this case, recombination results in longer one organismal generation), and in transgenerational in- shorter alleles of the protein. The length variation can have func- heritance in multicellular organisms. For example al- tional consequences. In FLo1, for example, longer alleles confer floc- though some phase variation in bacteria is due to changes yeast cells to each other to fo in genomic sequence(above), other cases rely on epig netic inheritance of methylation patterns. One of the bes confer gradually weaker flocculation, with the very shortest alleles studied examples is found in control of the pyelonephri- resulting in completely tis-associated pili(pap)operon by DNA methylation(Her day et al., 2002). Here, the on and off states are distin- not only of amino acid sequence but also at the DNA level, guished by methylation of Lrp-binding sites found which suggests the possibility of a beneficial outcome to proximal and distal, respectively, to the papBA promoter some rapid repeat variation that offsets the disadvantages the switch from on to off occurs at 10-4 per generation, used by pathogenic repeat variation(Verstrepen et al., whereas the converse switch occurs at 10per gener 2005) ation. An interesting example of heritable methylation- An interesting example of repeat variation that could mediated phenotypic variation in multicellular organisms conceivably prove beneficial in a population is found in in the flowering plant Linaria vulgaris. Naturally occurring a tandem repeat region upstream of the vasopressin re variation in methylation of the Lcyc gene distinguishes ceptor gene Avpr1a, which is known to influence sociobe peloric"morphological mutants with radial floral symme- havioral traits in voles (Hammock and Young, 2005 ). The try from the wild-type variant with bilateral floral symmetry repeat locus is highly variable in populations, which sug-(Cubas et al., 1999). The accelerated phenotypic variation gests an elevated mutation rate compared to that of other due to this"epimutation"may be adaptive in the context genomic regions(though the per-generation rate of repe of the rapid timescale of plant-pollinator coevolution. variation was not directly measured). Phenotypically, Another classic example of epigenetic inheritance is the pansion of this repeat region increases promoter acti silencing of subtelomeric genes in microorganisms. Yeast 658 Cell 128, 655-668, February 23, 2007 @2007 Elsevier Ind
not only of amino acid sequence but also at the DNA level, which suggests the possibility of a beneficial outcome to some rapid repeat variation that offsets the disadvantages caused by pathogenic repeat variation (Verstrepen et al., 2005). An interesting example of repeat variation that could conceivably prove beneficial in a population is found in a tandem repeat region upstream of the vasopressin receptor gene Avpr1a, which is known to influence sociobehavioral traits in voles (Hammock and Young, 2005). The repeat locus is highly variable in populations, which suggests an elevated mutation rate compared to that of other genomic regions (though the per-generation rate of repeat variation was not directly measured). Phenotypically, expansion of this repeat region increases promoter activity, and males with more repeat copies in the Avpr1a promoter show increased caretaking for their pups and increased pair bonding with partner females compared to individuals with fewer repeats. This repeat variation could therefore allow for rapid evolution of behavioral traits that may be of adaptive benefit in different environments. A second example of repeat-associated phenotypic plasticity that is seemingly not pathogenic was found by Fondon and Garner (Fondon and Garner, 2004). These authors demonstrate that repeat variability in the coding regions of the Alx-4 (aristaless-like 4) and Runx-2 (runt-related transcription factor) genes is associated with quantitative differences in limb and skull morphology in dogs. Hence, these repeats may allow rapid evolution of morphological variants on a conserved basic body plan that may provide an adaptive advantage as the selective environment changes. Epigenetic Inheritance and Rapid Phenotype Switching Another class of phenotypes vary at rates similar to, or often even higher than those typically generated by contingency loci. In most cases, this variation does not rely on mutations in the DNA sequence but rather relies on alternative, so-called ‘‘epigenetic’’ methods of inheritance. Like contingency loci, epigenetically heritable traits typically exhibit a limited repertoire of phenotypes and interconvert (‘‘switch’’) more rapidly than do phenotypes that change by point mutation. Epigenetic switches can be grouped according to the mechanism of inheritance, as epigenetic information is carried by substrates ranging from DNA methylation patterns to the folding of prion proteins. Methylation of DNA bases is one of the major mechanisms of epigenetic inheritance and has been implicated in phenotypic inheritance in unicellular organisms, in cell-state inheritance in multicellular organisms (during one organismal generation), and in transgenerational inheritance in multicellular organisms. For example, although some phase variation in bacteria is due to changes in genomic sequence (above), other cases rely on epigenetic inheritance of methylation patterns. One of the best studied examples is found in control of the pyelonephritis-associated pili (pap) operon by DNA methylation (Hernday et al., 2002). Here, the on and off states are distinguished by methylation of Lrp-binding sites found proximal and distal, respectively, to the papBA promoter. The switch from on to off occurs at 104 per generation, whereas the converse switch occurs at 102 per generation. An interesting example of heritable methylationmediated phenotypic variation in multicellular organisms is in the flowering plant Linaria vulgaris. Naturally occurring variation in methylation of the Lcyc gene distinguishes ‘‘peloric’’ morphological mutants with radial floral symmetry from the wild-type variant with bilateral floral symmetry (Cubas et al., 1999). The accelerated phenotypic variation due to this ‘‘epimutation’’ may be adaptive in the context of the rapid timescale of plant-pollinator coevolution. Another classic example of epigenetic inheritance is the silencing of subtelomeric genes in microorganisms. Yeast Figure 2. Recombination in Intragenic Repeats Certain genes, such as the S. cerevisiae FLO1 gene, contain tandem repeats within their coding sequences. These repeats are highly unstable and recombine at frequencies around 105 per (mitotic or meiotic) generation, resulting in the net loss or gain of repeat units. If the repeat units are not a multiple of three nucleotides, recombination gives rise to frameshifts, resulting in switching on and off of the gene. Most repeats found within open reading frames, however, are a multiple of three nucleotides long. In this case, recombination results in longer or shorter alleles of the protein. The length variation can have functional consequences. In FLO1, for example, longer alleles confer flocculation (i.e., the adhesion of yeast cells to each other to form a ‘‘floc’’ of cells that sediments in the medium; white arrow). Short FLO1 alleles confer gradually weaker flocculation, with the very shortest alleles resulting in completely planctonic (suspended) growth. 658 Cell 128, 655–668, February 23, 2007 ª2007 Elsevier Inc.
Cell Figure 3. A Model of the Inheritance of chromatin States Different nucleosome states hromatin states have been proposed to carry writable epigenetic information. Shown in Initiation of replication histone isoforms). After passage of the replica- tion fork, nucleosomes apparently segregate andomly to the two daughter chromosomes. oon thereafter, newly synthesized nucleo- omes (gray) are assembled onto the chromo- omes. in order for chromatin states to be her corporation of ne able for more than a handful of generations, synthesized nucleo w nucleosomes must be modified to the ame state as surrounding maternal nucleo- one possible model the proteins that associate with maternal nucle- somes locally instruct (arrows)new nucleo- ome feedback mechanism by which old Modification of new nucleosome telomeric regions contain multiple gene families, including ure 3). A similar p non occurs in the malaria patho- the cell-surface FLo genes, the thiamine-biosynthesis TH/ gen Plasmodium falciparum, where virulence factors such genes, and the hexose kinase HXK genes. Genes located as the erythrocyte-adhesion molecule PfEMP1 are er near telomeres are subject to variegated silencing; for coded subtelomerically and vary in expression from on example, a reporter gene adjacent to an artificially con- to off approximately every 50 generations(Roberts et al. structed telomere was shown to switch from on to off ap- 1992) in a Sir2-dependent manner. Stochastic subtelo- proximately every 10 to 15 generations( Gottschling et al meric switching of cell-surface genes of pathogens may 1990). Two related histone deacetylation mechanisms are help evade the host immune system, and presumably esponsible for subtelomeric silencing: genes immediately switching rates are tuned so that the time of exposure of proximal to the telomeres are silenced by the silent infor- an antigen is shorter than the time required for an effective mation-regulator Sir) complex, whereas genes located immune response somewhat more distant are silenced by Hda1 ( Gottschling Prions(proteins that can heritably occur in more than et aL., 1990; Halme et aL., 2004). Although the linkage be- one conformation) are fascinating examples of epige tween histone deacetylation and silencing is well estab- netic information carriers that are stable for relatively shed the mechanism of inheritance of chromatin states ng timescales. Prion proteins were originally described (both on and off) is still an active area of investigation(Fig- as infectious protein conformations that convert the ce128,655668. February23,2007@2007 Elsevier Inc.659
telomeric regions contain multiple gene families, including the cell-surface FLO genes, the thiamine-biosynthesis THI genes, and the hexose kinase HXK genes. Genes located near telomeres are subject to variegated silencing; for example, a reporter gene adjacent to an artificially constructed telomere was shown to switch from on to off approximately every 10 to 15 generations (Gottschling et al., 1990). Two related histone deacetylation mechanisms are responsible for subtelomeric silencing: genes immediately proximal to the telomeres are silenced by the silent information-regulator (Sir) complex, whereas genes located somewhat more distant are silenced by Hda1 (Gottschling et al., 1990; Halme et al., 2004). Although the linkage between histone deacetylation and silencing is well established, the mechanism of inheritance of chromatin states (both on and off) is still an active area of investigation (Figure 3). A similar phenomenon occurs in the malaria pathogen Plasmodium falciparum, where virulence factors such as the erythrocyte-adhesion molecule PfEMP1 are encoded subtelomerically and vary in expression from on to off approximately every 50 generations (Roberts et al., 1992) in a Sir2-dependent manner. Stochastic subtelomeric switching of cell-surface genes of pathogens may help evade the host immune system, and presumably switching rates are tuned so that the time of exposure of an antigen is shorter than the time required for an effective immune response. Prions (proteins that can heritably occur in more than one conformation) are fascinating examples of epigenetic information carriers that are stable for relatively long timescales. Prion proteins were originally described as infectious protein conformations that convert the Figure 3. A Model of the Inheritance of Chromatin States Chromatin states have been proposed to carry heritable epigenetic information. Shown in green and white are two different nucleosome states (possibly carrying distinct covalent modification patterns or distinct subsets of variant histone isoforms). After passage of the replication fork, nucleosomes apparently segregate randomly to the two daughter chromosomes. Soon thereafter, newly synthesized nucleosomes (gray) are assembled onto the chromosomes. In order for chromatin states to be heritable for more than a handful of generations, new nucleosomes must be modified to the same state as surrounding maternal nucleosomes. In one possible model of this feedback the proteins that associate with maternal nucleosomes locally instruct (arrows) new nucleosomes to carry the appropriate modification/ variant pattern. This model is one of several proposed, but all models have in common some feedback mechanism by which old nucleosomes influence the states of the newly synthesized nucleosomes deposited at a given locus. Cell 128, 655–668, February 23, 2007 ª2007 Elsevier Inc. 659