Applications of Population Genetics to Animal Breeding,from Wright,Fisher and Lush to Genomic Prediction

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives’ performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher’s infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with “genomic selection” is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.THE success of breeders in effecting immense changes in domesticated animals and plants greatly influenced Darwin’s insight into the power of selection and implications to evolution by natural selection. Following the Mendelian rediscovery, attempts were soon made to accommodate within the particulate Mendelian framework the continuous nature of many traits and the observation by Galton (1889) of a linear regression of an individual’s height on that of a relative, with the slope dependent on degree of relationship. A polygenic Mendelian model was first proposed by Yule (1902) (see Provine 1971; Hill 1984). After input from Pearson, Yule again, and Weinberg (who developed the theory a long way but whose work was ignored), its first full exposition in modern terms was by Ronald A. Fisher (1918) (biography by Box 1978). His analysis of variance partitioned the genotypic variance into additive, dominance and epistatic components. Sewall Wright (biography by Provine 1986) had by then developed the path coefficient method and subsequently (Wright 1921) showed how to compute inbreeding and relationship coefficients and their consequent effects on genetic variation of additive traits. His approach to relationship in terms of the correlation of uniting gametes may be less intuitive at the individual locus level than Malécot’s (1948) subsequent treatment in terms of identity by descent, but it transfers directly to the correlation of relatives for quantitative traits with additive effects.From these basic findings, the science of animal breeding was largely developed and expounded by Jay L. Lush (1896–1982) (see also commentaries by Chapman 1987 and Ollivier 2008). He was from a farming family and became interested in genetics as an undergraduate at Kansas State. Although his master’s degree was in genetics, his subsequent Ph.D. at the University of Wisconsin was in animal reproductive physiology. Following 8 years working in animal breeding at the University of Texas he went to Iowa State College (now University) in Ames in 1930. Wright was Lush’s hero: ‘I wish to acknowledge especially my indebtedness to Sewall Wright for many published and unpublished ideas upon which I have drawn, and for his friendly counsel” (Lush 1945, in the preface to his book Animal Breeding Plans). Lush commuted in 1931 to the University of Chicago to audit Sewall Wright’s course in statistical genetics and consult him. Speaking at the Poultry Breeders Roundtable in 1969: he said, “Those were by far the most fruitful 10 weeks I ever had.” (Chapman 1987, quoting A. E. Freeman). Lush was also exposed to and assimilated the work and ideas of R. A. Fisher, who lectured at Iowa State through the summers of 1931 and 1936 at the behest of G. W. Snedecor.Here I review Lush’s contributions and then discuss how animal breeding theory and methods have subsequently evolved. They have been based mainly on statistical methodology, supported to some extent by experiment and population genetic theory. Recently, the development of genomic methods and their integration into classical breeding theory has opened up ways to greatly enhance rates of genetic improvement. Lush focused on livestock improvement and spin-off into other areas was coincidental; but he had contact with corn breeders in Ames and beyond and made contributions to evolutionary biology and human genetics mainly through his developments in theory (e.g., Falconer 1965; Robertson 1966; Lande 1976, 1979; see also Hill and Kirkpatrick 2010). I make no attempt to be comprehensive, not least in choice of citations.     

The Modest Beginnings of One Genome Project

One of the top things on a geneticist’s wish list has to be a set of mutants for every gene in their particular organism. Such a set was produced for the yeast, Saccharomyces cerevisiae near the end of the 20th century by a consortium of yeast geneticists. However, the functional genomic analysis of one chromosome, its smallest, had already begun more than 25 years earlier as a project that was designed to define most or all of that chromosome’s essential genes by temperature-sensitive lethal mutations. When far fewer than expected genes were uncovered, the relatively new field of molecular cloning enabled us and indeed, the entire community of yeast researchers to approach this problem more definitively. These studies ultimately led to cloning, genomic sequencing, and the production and phenotypic analysis of the entire set of knockout mutations for this model organism as well as a better concept of what defines an essential function, a wish fulfilled that enables this model eukaryote to continue at the forefront of research in modern biology.THE yeast Saccharomyces cerevisiae genome project culminated with the first sequenced eukaryotic genome (Goffeau et al. 1996) and was followed by the functional analysis of almost all of its ∼6000 genes. This project produced a near complete collection of deletion mutants that among other things attempted to define the number of genes that were essential for growth of this organism in the laboratory (Winzeler et al. 1999; Giaever et al. 2002). These projects had their roots in earlier studies that began with Carl Lindegren’s first genetic map (Lindegren 1949; Lindegren et al. 1959), continued with the herculean efforts of Robert Mortimer and colleagues who compiled data from hundreds of investigators who had mapped genes (Mortimer and Hawthorne 1975; Mortimer and Schild 1980, 1985; Mortimer et al. 1989, 1992), and continues today through the Saccharomyces Genome Database led by Mike Cherry (Cherry et al. 1997, 2012) that curates genomic and related information.This is the story of the first functional genomic analysis of yeast, which began with our investigation of chromosome I, the organism’s smallest. It is worth telling because it traces its routes to the “phage school” and shows how what was deemed non-hypothesis–driven research and data collection were actually tied to a fundamental question and indeed to several hypotheses. It is also about how sharing information, unselfish donation of materials, and true collaboration within the yeast community enabled this organism to rise to the forefront of molecular biology. It is told with the idea that it really does take a village to decipher a genome.     

TRAF2 is a biologically important necroptosis suppressor

Tumor necrosis factor α (TNFα) triggers necroptotic cell death through an intracellular signaling complex containing receptor-interacting protein kinase (RIPK) 1 and RIPK3, called the necrosome. RIPK1 phosphorylates RIPK3, which phosphorylates the pseudokinase mixed lineage kinase-domain-like (MLKL)—driving its oligomerization and membrane-disrupting necroptotic activity. Here, we show that TNF receptor-associated factor 2 (TRAF2)—previously implicated in apoptosis suppression—also inhibits necroptotic signaling by TNFα. TRAF2 disruption in mouse fibroblasts augmented TNFα–driven necrosome formation and RIPK3-MLKL association, promoting necroptosis. TRAF2 constitutively associated with MLKL, whereas TNFα reversed this via cylindromatosis-dependent TRAF2 deubiquitination. Ectopic interaction of TRAF2 and MLKL required the C-terminal portion but not the N-terminal, RING, or CIM region of TRAF2. Induced TRAF2 knockout (KO) in adult mice caused rapid lethality, in conjunction with increased hepatic necrosome assembly. By contrast, TRAF2 KO on a RIPK3 KO background caused delayed mortality, in concert with elevated intestinal caspase-8 protein and activity. Combined injection of TNFR1-Fc, Fas-Fc and DR5-Fc decoys prevented death upon TRAF2 KO. However, Fas-Fc and DR5-Fc were ineffective, whereas TNFR1-Fc and interferon α receptor (IFNAR1)-Fc were partially protective against lethality upon combined TRAF2 and RIPK3 KO. These results identify TRAF2 as an important biological suppressor of necroptosis in vitro and in vivo.Apoptotic cell death is mediated by caspases and has distinct morphological features, including membrane blebbing, cell shrinkage and nuclear fragmentation.^{1, 2, 3, 4} In contrast, necroptotic cell death is caspase-independent and is characterized by loss of membrane integrity, cell swelling and implosion.^{1, 2, 5} Nevertheless, necroptosis is a highly regulated process, requiring activation of RIPK1 and RIPK3, which form the core necrosome complex.^{1, 2, 5} Necrosome assembly can be induced via specific death receptors or toll-like receptors, among other modules.^{6, 7, 8, 9} The activated necrosome engages MLKL by RIPK3-mediated phosphorylation.^{6, 10, 11} MLKL then oligomerizes and binds to membrane phospholipids, forming pores that cause necroptotic cell death.^{10, 12, 13, 14, 15} Unchecked necroptosis disrupts embryonic development in mice and contributes to several human diseases.^{7, 8, 16, 17, 18, 19, 20, 21, 22}The apoptotic mediators FADD, caspase-8 and cFLIP suppress necroptosis.^{19, 20, 21, 23, 24} Elimination of any of these genes in mice causes embryonic lethality, subverted by additional deletion of RIPK3 or MLKL.^{19, 20, 21, 25} Necroptosis is also regulated at the level of RIPK1. Whereas TNFα engagement of TNFR1 leads to K63-linked ubiquitination of RIPK1 by cellular inhibitor of apoptosis proteins (cIAPs) to promote nuclear factor (NF)-κB activation,²⁶ necroptosis requires suppression or reversal of this modification to allow RIPK1 autophosphorylation and consequent RIPK3 activation.^{2, 23, 27, 28} CYLD promotes necroptotic signaling by deubiquitinating RIPK1, augmenting its interaction with RIPK3.²⁹ Conversely, caspase-8-mediated CYLD cleavage inhibits necroptosis.²⁴TRAF2 recruits cIAPs to the TNFα-TNFR1 signaling complex, facilitating NF-κB activation.^{30, 31, 32, 33} TRAF2 also supports K48-linked ubiquitination and proteasomal degradation of death-receptor-activated caspase-8, curbing apoptosis.³⁴ TRAF2 KO mice display embryonic lethality; some survive through birth but have severe developmental and immune deficiencies and die prematurely.^{35, 36} Conditional TRAF2 KO leads to rapid intestinal inflammation and mortality.³⁷ Furthermore, hepatic TRAF2 depletion augments apoptosis activation via Fas/CD95.³⁴ TRAF2 attenuates necroptosis induction in vitro by the death ligands Apo2L/TRAIL and Fas/CD95L.³⁸ However, it remains unclear whether TRAF2 regulates TNFα-induced necroptosis—and if so—how. Our present findings reveal that TRAF2 inhibits TNFα necroptotic signaling. Furthermore, our results establish TRAF2 as a biologically important necroptosis suppressor in vitro and in vivo and provide initial insight into the mechanisms underlying this function.     

Georges Teissier (1900–1972) and the Modern Synthesis in France

This Perspectives is devoted to the ideas of the French zoologist Georges Teissier about the mechanisms of evolution and the relations between micro- and macroevolution. Working in an almost universally neo-Lamarckian context in France, Teissier was one of the very few Darwinians there at the time of the evolutionary synthesis. The general atmosphere of French zoology during the 1920s and the 1930s will first be recalled, to understand the specific conditions in which Teissier became a zoologist. After a brief overview of his joint work with Philippe L’Héritier on the experimental genetics of Drosophila, this article describes the ways Teissier, during the 1950s, conceptualized the mechanisms that could allow for macroevolutionary transitions.IT is usually acknowledged that France did not significantly participate in the elaboration of 20th century evolutionary theory, often designated The Modern Synthesis. In their classical book on the history of the synthesis, Ernst Mayr and William B. Provine devoted a whole—nonetheless small—chapter to this specific issue (Mayr and Provine 1998, pp. 309–328). Mayr clearly stated that “France is the only major scientific nation that did not contribute significantly to the evolutionary synthesis” (Mayr 1998, p. 309). In the absence of a French architect of the synthesis, Mayr and Provine asked Ernest Boesiger, a Swiss population geneticist and a former student of Georges Teissier, to tell the story of what had happened in French biology at the time of the evolutionary synthesis. Boesiger, who died in 1975, wrote a paper in 1974 that provided the firm basis of the chapter. In very strong terms, he depicted French biology as “a kind of living fossil in the rejection of modern evolutionary theories” (Boesiger 1998, p. 309). He insisted on the fact that, even in 1974, most French biologists and philosophers were still reluctant to accept Darwinism. As regards the period of the 1930s, Boesiger was able to think of only two exceptions: Georges Teissier and Philippe L’Héritier. He then referred to their joint research in population genetics, which was based on the new technique of the population cages with the species Drosophila melanogaster, and listed their contributions to this new discipline.If Teissier and L’Héritier’s works on Drosophila are nowadays more widely recognized than in 1974, due in particular to the efforts of Jean Gayon and Michel Veuille (Gayon and Veuille 2001), this recognition could have as an unintended consequence the reduction of both Teissier and L’Héritier to being simply the inventors of a useful technique, namely the population cages (see especially how Mayr presented their work in his other classical book, Mayr 1982, p. 574), or as the founders of a French school of population geneticists (Gayon and Veuille 2001). The aim of this article is to reevaluate the way Georges Teissier (1900–1972) conceived Darwinian natural selection not only as an important mechanism for evolution at the population level but more fundamentally as a general key for the unification of biology, exactly as Julian Huxley or Ernst Mayr did during the same period (1930–1970). However, starting in the early 1950s, Teissier went on to conceive a very specific understanding of the evolutionary synthesis.In this article, I will first describe the general atmosphere of evolutionary issues in French biology at the time when Teissier started working as a zoologist, to understand against what he developed his joint research program with L’Héritier and afterward his general conceptions about evolution. During the 1930s and the 1940s, only a very few scientists in France could be seen as Darwinians. In addition to Teissier and L’Héritier, one may also consider Marcel Prenant, Boris Ephrussi, and the mathematician Gustave Malécot. Building on Jean Gayon and Michel Veuille’s work, I will then give a quick overview of L’Héritier and Teissier’s most important achievements in the field of population genetics. In the third part, I will discuss the discovery made by Teissier and L’Héritier of a case of cytoplasmic inheritance in Drosophila. This unexpected finding led them into the field of non-Mendelian heredity. I will then develop in detail the way Teissier finally went on to conceive the relation between microevolution and macroevolution, in light of the general context of French biology and of the development of the field of cytoplasmic inheritance.     

Louis Reichardt: The long climb to science's summits

From the highest mountains to biology''s own Everest—the brain—Reichardt tackles the biggest challenges of climbing and biology.Louis Reichardt''s scientific career has spanned the simple and the complex. As a graduate student, Reichardt helped to uncover the now renowned DNA regulatory mechanisms that allow one of the simplest life forms, lambda phage, either to hide within a cell or to make its presence known via massive replication (1).Open in a separate windowLouis ReichardtLetting his curiosity for the unknown guide him, Reichardt then forayed into a much more intricate system, the brain. As a postdoc, he showed that growth conditions influenced which neurotransmitters are synthesized by isolated neurons (2). Later, as a professor at UCSF, he discovered synaptotagmin, using the first monoclonal antibody that defined a synaptic vesicle membrane protein (3), showed that expression levels of nerve growth factor in target tissues correlate with the density of innervation (4), and characterized the properties of mice lacking genes encoding the neurotrophins and their Trk receptors (5, 6).Now a professor and director of the Neuroscience Program at UCSF, Reichardt''s laboratory still studies the interface of cell biology and neurobiology, including the involvement of cell adhesion molecules in synaptic development (7). He explains that science is not so different from his favorite hobby, mountain climbing.

“The gist of it was ‘Others more foolish might try this, but it was not for us.’ I thought, ‘We''ll obviously have to do it.’”

     

Polymorphic Genes of Major Effect: Consequences for Variation,Selection and Evolution in Arabidopsis thaliana

The importance of genes of major effect for evolutionary trajectories within and among natural populations has long been the subject of intense debate. For example, if allelic variation at a major-effect locus fundamentally alters the structure of quantitative trait variation, then fixation of a single locus can have rapid and profound effects on the rate or direction of subsequent evolutionary change. Using an Arabidopsis thaliana RIL mapping population, we compare G-matrix structure between lines possessing different alleles at ERECTA, a locus known to affect ecologically relevant variation in plant architecture. We find that the allele present at ERECTA significantly alters G-matrix structure—in particular the genetic correlations between branch number and flowering time traits—and may also modulate the strength of natural selection on these traits. Despite these differences, however, when we extend our analysis to determine how evolution might differ depending on the ERECTA allele, we find that predicted responses to selection are similar. To compare responses to selection between allele classes, we developed a resampling strategy that incorporates uncertainty in estimates of selection that can also be used for statistical comparisons of G matrices.THE structure of the genetic variation that underlies phenotypic traits has important consequences for understanding the evolution of quantitative traits (Fisher 1930; Lande 1979; Bulmer 1980; Kimura 1983; Orr 1998; Agrawal et al. 2001). Despite the infinitesimal model''s allure and theoretical tractability (see Orr and Coyne 1992; Orr 1998, 2005a,b for reviews of its influence), evidence has accumulated from several sources (artificial selection experiments, experimental evolution, and QTL mapping) to suggest that genes of major effect often contribute to quantitative traits. Thus, the frequency and role of genes of major effect in evolutionary quantitative genetics have been a subject of intense debate and investigation for close to 80 years (Fisher 1930; Kimura 1983; Orr 1998, 2005a,b). Beyond the conceptual implications, the prevalence of major-effect loci also affects our ability to determine the genetic basis of adaptations and species differences (e.g., Bradshaw et al. 1995, 1998).Although the existence of genes of major effect is no longer in doubt, we still lack basic empirical data on how segregating variation at such genes affects key components of evolutionary process (but see Carrière and Roff 1995). In other words, How does polymorphism at genes of major effect alter patterns of genetic variation and covariation, natural selection, and the likely response to selection? The lack of data stems, in part, from the methods used to detect genes of major effect: experimental evolution (e.g., Bull et al. 1997; Zeyl 2005) and QTL analysis (see Erickson et al. 2004 for a review) often detect such genes retrospectively after they have become fixed in experimental populations or the species pairs used to generate the mapping population. The consequences of polymorphism at these genes on patterns of variation, covariation, selection, and the response to selection—which can be transient (Agrawal et al. 2001)—are thus often unobserved.A partial exception to the absence of data on the effects of major genes comes from artificial selection experiments, in which a substantial evolutionary response to selection in the phenotype after a plateau is often interpreted as evidence for the fixation of a major-effect locus (Frankham et al. 1968; Yoo 1980a,b; Frankham 1980; Shrimpton and Robertson 1988a,b; Caballero et al. 1991; Keightley 1998; see Mackay 1990 and Hill and Caballero 1992 for reviews). However, many of these experiments report only data on the selected phenotype (e.g., bristle number) or, alternatively, the selected phenotype and some measure of fitness (e.g., Frankham et al. 1968, Yoo 1980b; Caballero et al. 1991; Mackay et al. 1994; Fry et al. 1995; Nuzhdin et al. 1995; Zur Lage et al. 1997), making it difficult to infer how a mutation will affect variation, covariation, selection, and evolutionary responses for a suite of traits that might affect fitness themselves. One approach is to document how variation at individual genes of major effect affects the genetic variance–covariance matrix (“G matrix”; Lande 1979), which represents the additive genetic variance and covariance between traits.Although direct evidence for variation at major-effect genes altering patterns of genetic variation, covariation, and selection is rare, there is abundant evidence for the genetic mechanisms that could produce these dynamics. A gene of major effect could have these consequences due to any of at least three genetic mechanisms: (1) pleiotropy, where a gene of major effect influences several traits, including potentially fitness, simultaneously, (2) physical linkage or linkage disequilibrium (LD), in which a gene of major effect is either physically linked or in LD with other genes that influence other traits under selection, and (3) epistasis, in which the allele present at a major-effect gene alters the phenotypic effect of other loci and potentially phenotypes under selection. Evidence for these three evolutionary genetic mechanisms leading to changes in suites of traits comes from a variety of sources, including mutation accumulation experiments (Clark et al. 1995; Fernandez and Lopez-Fanjul 1996), mutation induction experiments (Keightley and Ohnishi 1998), artificial selection experiments (Long et al. 1995), and transposable element insertions (Rollmann et al. 2006). For pleiotropy in particular, major-effect genes that have consequences on several phenotypic traits are well known from the domestication and livestock breeding literature [e.g., myostatin mutations in Belgian blue cattle and whippets (Arthur 1995; Grobet et al. 1997; Mosher et al. 2007), halothane genes in pigs (Christian and Rothschild 1991; Fujii et al. 1991), and Booroola and Inverdale genes in sheep (Amer et al. 1999; Visscher et al. 2000)]. While these data suggest that variation at major-effect genes could—and probably does—influence variation, covariation, and selection on quantitative traits, data on the magnitude of these consequences remain lacking.Recombinant inbred line (RIL) populations are a promising tool for investigating the influence of major-effect loci. During advancement of the lines from F₂''s to RILs, alternate alleles at major-effect genes (and most of the rest of the genome) will be made homozygous, simplifying comparisons among genotypic classes. Because of the high homozygosity, individuals within RILs are nearly genetically identical, facilitating phenotyping of many genotypes under a range of environments. In addition, because of recombination, alternative alleles are randomized across genetic backgrounds—facilitating robust comparisons between sets of lines differing at a major-effect locus.Here we investigate how polymorphism at an artificially induced mutation, the erecta locus in Arabidopsis thaliana, affects the magnitude of these important evolutionary genetic parameters under ecologically realistic field conditions. We use the Landsberg erecta (Ler) × Columbia (Col) RIL population of A. thaliana to examine how variation at a gene of major effect influences genetic variation, covariation, and selection on quantitative traits in a field setting. The Ler × Col RIL population is particularly suitable, because it segregates for an artificially induced mutation at the erecta locus, which has been shown to influence a wide variety of plant traits. The Ler × Col population thus allows a powerful test of the effects of segregating variation at a gene—chosen a priori—with numerous pleiotropic effects. The ERECTA gene is a leucine-rich receptor-like kinase (LRR-RLK) (Torii et al. 1996) and has been shown to affect plant growth rates (El-Lithy et al. 2004), stomatal patterning and transpiration efficiency (Masle et al. 2005; Shpak et al. 2005), bacterial pathogen resistance (Godiard et al. 2003), inflorescence and floral organ size and shape (Douglas et al. 2002; Shpak et al. 2003, 2004), and leaf polarity (Xu et al. 2003; Qi et al. 2004).Specifically, we sought to answer the following questions: (1) Is variation at erecta significantly associated with changes to the G matrix? (2) Is variation at erecta associated with changes in natural selection on genetically variable traits? And (3) is variation at erecta associated with significantly different projected evolutionary responses to selection?     

Is transdifferentiation in trouble?

Spectacular examples of transdifferentiation—such as brain cells turning to blood and blood to brain—have given way to sneaking suspicions about artifacts in culture, fusion, and clonality. Could cell fates be relatively fixed after all?The exact fate map of Caenorhabditis elegans is a dream for developmental biology control freaks. Stem cells, which can both self-renew and produce a particular set of differentiated progeny, seemed like the mammalian equivalent, albeit a little more malleable and messy.But lately this view of development as a controlled series of fate maps, with each cell type performing one and only one function, has come under attack. Stem cell researchers have claimed that brain can be turned into blood (Bjornson et al., 1999), and blood into brain (Brazelton et al., 2000; Mezey et al., 2000; Priller et al., 2001), muscle (Ferrari et al., 1998), myocardium (Orlic et al., 2001) or liver (Lagasse et al., 2000). In the hands of some investigators, bone marrow (Krause et al., 2001) and neural stem cells (Clarke et al., 2000) can apparently turn into pretty much everything.“I believe this phenomenon was missed for a very long time,” says Helen Blau (Stanford University, Stanford, CA), “because we didn''t have the imagination to think that it could happen or the tools to prove it.” The orgy of plasticity has prompted Blau to propose a new way of thinking about cell fate determination (Blau et al., 2001), with stem cell capability existing as a switch that can be turned on and altered as easily as an apoptotic program.But others see at least some of the reports of cell fate changes—also known as transdifferentiation—as simply sloppy science. “The emphasis,” says Sean Morrison (University of Michigan, Ann Arbor, MI), “has been on describing spectacular results rather than on describing it in an evenhanded way that would identify the real importance of the phenomenon.” Several dissenters have called for more stringent criteria for judging transdifferentiation experiments (Anderson, 2001; Anderson et al., 2001).As follow-up studies emerge, questions have been raised about some of the earlier results. The verdict is not in yet, but the resolution of this debate will have implications on several fronts, determining our view of basic stem cell biology, affecting the strategy for treating patients with stem cell therapies, and influencing the steps used by the United States Congress to regulate stem cell research.     

Interactions Among Flower-Size QTL of Mimulus guttatus Are Abundant but Highly Variable in Nature

The frequency and character of interactions among genes influencing complex traits remain unknown. Our ignorance is most acute for segregating variation within natural populations, the epistasis most relevant for quantitative trait evolution. Here, we report a comprehensive survey of interactions among a defined set of flower-size QTL: loci polymorphic within a single natural population of yellow monkeyflower (Mimulus guttatus). We find that epistasis is typical. Observed phenotypes routinely differ from those predicted on the basis of direct allelic affects in the isogenic background, although the direction of deviations is highly variable. Across QTL pairs, there are significantly positive and negative interactions for every trait. Across traits, specific locus pairs routinely exhibit both positive and negative interactions. There was a tendency for negative epistasis to accompany positive direct effects and vice versa for the trait of corolla width, which may be due, at least in part, to the fact that QTL were identified from their direct effects on this trait.EPISTASIS contributes significantly to intrapopulation variation in floral morphology, development time, and male fitness components of Mimulus guttatus (Kelly 2005). The aggregate effect of interactions among QTL substantially alters the resemblance of relatives and phenotypic response to inbreeding. However, previous experiments did not identify the specific character of interactions between QTL. For example, it is not clear whether interactions change the rank order of QTL genotypes. If the direction of allelic effect changes with genetic background, so-called “sign epistasis” (Weinreich et al. 2005), the same selection pressure may favor different alleles in different genomic contexts, e.g., different subpopulations of a species (De Brito et al. 2005). When the trait is fitness, these kinds of interactions naturally generate peaks and valleys in genotypic fitness landscapes (Wright 1932; Burch and Chao 2000).Sign epistasis can involve a reversal of allelic effect at one or both loci of an interacting pair. Poelwijk et al. (2007) define the double reversal as “reciprocal sign epistasis” and contrast sign epistasis generally to “magnitude epistasis” where the magnitude but not the direction of allelic effects changes with genetic background. Magnitude epistasis includes synergistic interactions (alleles have greater effect in combination than individually) and less-than-additive or diminishing returns interactions (alleles have lesser effect in combination) (see Crow and Kimura 1970). Alternatively, one can classify interactions as positive or negative (Phillips et al. 2000)—positive if the observed phenotype of an allelic combination exceeds that predicted from direct effects at each locus, and negative if the phenotype of the combination is less than the additive prediction. Unfortunately, there is no simple logical mapping from the magnitude/sign epistasis classification to the positive/negative classification, nor to epistasis in the classical sense (Bateson 1909), wherein one locus masks the effect of another. The taxonomy of epistasis is further complicated by dominance (Routman and Cheverud 1997), higher-order interactions (Templeton 2000), and environmental dependencies (Brock et al. 2010).Molecular genetic studies provide clear examples of sign epistasis. Here, the interacting polymorphisms are often within the same gene. For example, the stability of RNA secondary structures requires matching of nucleotides at different positions. Whether a particular nucleotide change increases or reduces stability depends entirely on the identity of the nucleotide at its paired site (Chen et al. 1999). With “compensatory evolution” (Moore et al. 2000), a mutation that is neutral or detrimental in the original genetic background becomes advantageous by compensating for some other mutation that has recently fixed or at least become prevalent within the population. Mutations conferring antibiotic resistance often have deleterious side effects that reduce bacterial fitness in the absence of the drug. These side effects are attenuated by secondary mutations that are often detrimental in the original genotype (Levin et al. 1997; Schrag et al. 1997).Despite the progress in research on microbes (Weinreich et al. 2005; Elena et al. 2010), we currently know little about the prevalence or nature of epistasis for quantitative traits (Carlborg and Haley 2004) and particularly its impact on standing (segregating) variation within natural populations. Eshed and Zamir (1996) found extensive epistasis among QTL in Lycopersicon (tomato), but interactions primarily influenced the magnitude of single-locus effects and not their direction. In contrast, Kroymann and Mitchell-Olds (2005) documented a case of QTL effect reversal in Arabidopsis thaliana. The high allele for biomass accumulation in the Ler-0 accession becomes the low allele when introgressed into another line (the Col-0 accession). Patterns of gene sequence variation suggest that this polymorphism is maintained by balancing selection. In Avena barbata, two loci with negligible average effects exhibit sign epistasis for fitness in a cross between mesic and xeric genotypes (Latta et al. 2010).Even with these examples, it is difficult to evaluate the quantitative frequency of epistasis or regularities in its nature from the current literature. In part, this is because discovery of QTL×QTL interactions is typically idiosyncratic. In a segregating mapping population such as F₂’s or recombinant inbred lines, there is limited replication of particular multi-locus genotypes. As a consequence, it is difficult to accurately estimate the mean phenotype of any particular multi-locus genotype. Also, there are an enormous number of pairwise tests in a full simultaneous scan, which leads to very stringent significance levels (see box 2 of Carlborg and Haley 2004). Thus, while genomic scans have successfully identified interactions (e.g., Li et al. 1997; Cheverud 2000; Montooth et al. 2003), it is hard to know if the many nonsignificant interactions are due to absence of effect or absence of power.Our intention in this study was to conduct a comprehensive survey of interactions among a specific set of monkeyflower QTL. These loci are polymorphic within a single contiguous natural population. We first mapped flower-size QTL within nearly isogenic lines (NILs). Measurements on the NILs isolate the “direct effect” of each QTL, i.e., how the polymorphism affects phenotype in a single uniform genetic background. We then intercrossed the various single-QTL NILs in all combinations to generate the four double homozygotes for each pair of QTL: aabb, AAbb, aaBB, and AABB. This design allows high replication of each multi-locus genotype and hence reasonable power to detect even moderate epistasis (e.g., Moyle and Nakazato 2009). Applying this methodology, we find that epistasis is more the rule than the exception, although the form of interaction is highly variable among QTL pairs.     

Under Tension: Kinetochores and Basic Research

The Genetics Society of America’s Edward Novitski Prize recognizes an extraordinary level of creativity and intellectual ingenuity in the solution of significant problems in genetics research. The 2015 winner, Sue Biggins, has made significant contributions to our understanding of how chromosomes attach to the mitotic spindle, a process essential for cell division and frequently impaired in cancer. Among other achievements, Biggins was the first to demonstrate that the Aurora B protein kinase is a key regulator of kinetochore function and that chromatin composition and centromere identity can be regulated by histone proteolysis. In 2010, Biggins and her colleagues were the first to purify kinetochores and, using this system, have already made several groundbreaking discoveries about the function and structure of these crucial components of the segregation machinery.Open in a separate windowIt is easy to forget that basic research on a simple model organism has led to many fundamental insights about how cells work and what goes wrong in disease, especially with the continual pressure from funding agencies and institutions to perform translational research. It is also easy to make the mistake of thinking that all major discoveries using model organisms have already been made.In his Novitski prize essay last year, Charlie Boone noted that the yeast Saccharomyces cerevisiae is better understood than any other cell. This year, I am honored to receive the same award for work that exploited yeast’s powerful combination of relative simplicity and strong conservation of function. In a collaborative effort with Chip Asbury’s lab, we reconstituted kinetochore–microtubule attachments that withstand tension in vitro for the first time (Akiyoshi et al. 2010). Our work is an example of how yeast can provide unexpected insights into conserved processes, and why it is important to support scientists in exploring new directions.Many key discoveries about cell division were initially made using budding yeast. Centromeres were first identified and cloned from yeast, and this information was critical to constructing the first artificial chromosome (Clarke and Carbon 1980; Murray and Szostak 1983; Bloom 2015). Over the years, yeast genetic screens have identified most kinetochore components as well as the key pathways that regulate chromosome segregation (for reviews, see Biggins 2013; Malvezzi and Westermann 2014). Cell-cycle checkpoints were first demonstrated in this organism (Weinert and Hartwell 1988), and the majority of conserved spindle checkpoint genes were identified in two seminal genetic yeast screens (Hoyt et al. 1991; Li and Murray 1991).

Isolating intact kinetochores was an intellectual and technical tour-de-force that laid the groundwork for mechanistic and proteomic analysis of kinetochore proteins. Sue Biggins’ perseverance and intellectual creativity in pursuing this question produced extraordinary insights into how kinetochores interact with microtubules.— Needhi Bhalla, University of California, Santa Cruz

It has been known for decades that chromosome segregation in all organisms relies on the tension-dependent stabilization of proper kinetochore–microtubule attachments (for review, see Nicklas and Ward 1994). This behavior was attributed to a protein kinase-mediated error correction mechanism that destabilizes incorrect attachments because they lack tension (for review, see van der Horst and Lens 2014). To ultimately understand how tension regulates the kinase, I decided that we needed a system for directly manipulating tension on kinetochore–microtubule attachments in vitro. However, kinetochores had never been isolated from any organism; we were far from testing the regulation of error correction in vitro.I was trained as a geneticist, not a biochemist. However, the supportive culture at the “Hutch” (Fred Hutchinson Cancer Research Center) helped our lab to take a risk on something new. Bungo Akiyoshi (now at the University of Oxford) developed the first technique with which to purify the core yeast kinetochore (Akiyoshi et al. 2010). We got a lot of advice and support from colleagues at the Hutch with biochemistry expertise, especially from Toshi Tsukiyama. Once Bungo had optimized a protocol by which to purify kinetochores, our next step was to develop a technique for binding these kinetochores to microtubules and putting them under tension. Fortunately, we are located near Chip Asbury’s lab at the University of Washington, which pioneered laser-trapping techniques to study kinetochore proteins (Asbury et al. 2006; Franck et al. 2007; Powers et al. 2009). Together, our labs used the purified kinetochores to reconstitute kinetochore–microtubule attachments under tension. Our reconstitution system does not include the error correction kinase or any additional cellular factors, so we were surprised to find that the kinetochore–microtubule attachments were stabilized directly by tension (Akiyoshi et al. 2010). Discoveries come from unexpected places—this was preliminary work intended as the foundation for analyzing how tension regulates the error-correction kinase.This finding helps to explain one aspect of the long-standing observation that attachments under tension in vivo are stable. We do not yet know whether and how often the aneuploidy that is a hallmark of so many cancers is due to alterations in kinetochore function, but this reconstitution system can now be applied to understanding the properties of kinetochores in other organisms and in cancer cells. We have also started to use our purification technique to address other aspects of kinetochore function and structure (Gonen et al. 2012; London et al. 2012; Sarangapani et al. 2013; London and Biggins 2014; Sarangapani et al. 2014).

It is […] easy to make the mistake of thinking that all major discoveries using model organisms have already been made. —S.B.

When I first started this work, my grant renewal application did not get a fundable score because of the risky nature of the project and the lack of convincing preliminary data. Luckily, I had colleagues at the Hutch who supported our attempts to do something new despite our lack of expertise. Funding agencies often dismiss applications when the investigator isn’t well versed in the necessary skills, and it is difficult for investigators to obtain money to initiate pilot projects. The current movement of the National Institute of General Medical Sciences and other institutes at the National Institutes of Health to fund investigator-initiated research as well as project-based research is a step in the right direction. We also need to promote collaborations that can help move fields forward, to integrate genetics with other disciplines, and to foster an environment where scientists can try something new. Research in model organisms will continue to provide unpredictable insights into biological processes, especially if we stay open minded to the research we fund and we continue to support investigators who take on new endeavors.     

Flexible open conformation of the AP-3 complex explains its role in cargo recruitment at the Golgi

Vesicle formation at endomembranes requires the selective concentration of cargo by coat proteins. Conserved adapter protein complexes at the Golgi (AP-3), the endosome (AP-1), or the plasma membrane (AP-2) with their conserved core domain and flexible ear domains mediate this function. These complexes also rely on the small GTPase Arf1 and/or specific phosphoinositides for membrane binding. The structural details that influence these processes, however, are still poorly understood. Here we present cryo-EM structures of the full-length stable 300 kDa yeast AP-3 complex. The structures reveal that AP-3 adopts an open conformation in solution, comparable to the membrane-bound conformations of AP-1 or AP-2. This open conformation appears to be far more flexible than AP-1 or AP-2, resulting in compact, intermediate, and stretched subconformations. Mass spectrometrical analysis of the cross-linked AP-3 complex further indicates that the ear domains are flexibly attached to the surface of the complex. Using biochemical reconstitution assays, we also show that efficient AP-3 recruitment to the membrane depends primarily on cargo binding. Once bound to cargo, AP-3 clustered and immobilized cargo molecules, as revealed by single-molecule imaging on polymer-supported membranes. We conclude that its flexible open state may enable AP-3 to bind and collect cargo at the Golgi and could thus allow coordinated vesicle formation at the trans-Golgi upon Arf1 activation.

Eukaryotic cells have membrane-enclosed organelles, which carry out specialized functions, including compartmentalized biochemical reactions, metabolic channeling, and regulated signaling, inside a single cell. The transport of proteins, lipids, and other molecules between these organelles is mediated largely by small vesicular carriers that bud off at a donor compartment and fuse with the target membrane to deliver their cargo. The generation of these vesicles has been subject to extensive studies and has led to the identification of numerous coat proteins that are required for their formation at different sites (1, 2). Coat proteins can be monomers, but in most cases, they consist of several proteins, which form a heteromeric complex.Heterotetrameric adapter protein (AP) complexes are required at several endomembranes for cargo binding. Five well-conserved AP-complexes with differing functions have been identified in mammalian cells, named AP-1–AP-5, of which three (AP-1–AP-3) are conserved from yeast to human (3, 4). The three conserved adapter complexes function at different membranes along the endomembrane system. AP-1 is required for cargo transport between the Golgi and the endosome, AP-2 is required for cargo recognition and transport between the plasma membrane and the early endosome. Finally, AP-3 functions between the trans Golgi and the vacuole in yeast, whereas mammalian AP-3 localizes to a tubular endosomal compartment, in addition to or instead of the TGN (2, 5, 6).Each of the complexes consists of four different subunits: two large adaptins (named α−ζ and β1-5 respectively), a medium-sized subunit (μ1-5), and a small subunit (σ1-5). While μ- and σ-subunits together with the N-termini of the large adaptins build the membrane-binding core of the complex, the C-termini of both adaptins contain the ear domains, which are connected via flexible linkers (2). The recruitment of these complexes to membranes is not entirely conserved. They all require cargo binding, yet AP-1 binds Arf1-GTP with the γ and β1 subunit and phosphatidylinositol-4-phosphate (PI4P) via a proposed conserved site on its γ-subunit (7, 8). AP-2, on the other hand, interacts with PI(4,5)P₂ at the plasma membrane via its α, β2, and μ2 subunits (9, 10, 11).Several studies have uncovered how AP-3 functions in cargo sorting in yeast. AP-3 recognizes cargo at the Golgi via two sorting motifs in the cytosolic segments of membrane proteins: a Yxxφ sorting motif, as found in yeast in the SNARE Nyv1 or the Yck3 casein kinase, which binds to a site in μ3, as shown for mammalian AP-3, which is similar to μ2 in AP-2 (12, 13, 14), and dileucine motifs as found in the yeast SNARE Vam3 or the alkaline phosphatase Pho8, potentially also at a site comparable to AP-1 and AP-2 (15, 16). Unlike AP-1 and AP-2-coated vesicles, which depend on clathrin for their formation (2, 17), AP-3 vesicle formation in yeast does not require clathrin or the HOPS subunit Vps41 (18), yet Vps41 is required at the vacuole to bind AP-3 vesicles prior to fusion (19, 20, 21, 22). Studies in metazoan cells revealed that Vps41 and AP-3 function in regulated secretion (23, 24, 25), and AP-3 is required for biogenesis of lysosome-related organelles (26). This suggests that the AP-3 complex has features that are quite different from AP-1 and AP-2 complexes, which cooperate with clathrin in vesicle formation (2).Among the three conserved AP complexes, the function of the AP-3 complex is the least understood. Arf1 is necessary for efficient AP-3 vesicle generation in mammalian cells and shows a direct interaction with the β3 and δ subunits of AP-3 (27, 28). In addition, in vitro experiments on mammalian AP-3 using liposomes or enriched Golgi membranes suggest Arf1 as an important factor in AP-3 recruitment, whereas acidic lipids do not have a major effect, in contrast to what was found for AP-1 and AP-2 (7, 11, 29, 30). Another study showed that membrane recruitment of AP-3 depends on the recognition of sorting signals in cargo tails and PI3P (31), similar to AP-1 recruitment via cargo tails, Arf1 and PI4P (32).However, since AP-1 and AP-3 are both recruited to the trans-Golgi network (TGN) in yeast (33), the mechanism of their recruitment likely differs. Even though Arf1 is required, yeast AP-3 seems to be present at the TGN before the arrival of the Arf1 guanine nucleotide exchange factor (GEF) Sec7 (33). This implies the necessity for additional factors at the TGN and a distinct mechanism to allow for spatial and temporal separation of AP-1 and AP-3 recruitment to membranes. Structural data on mammalian AP-1 and AP-2 “core” complexes without the hinge and ear domains of their large subunits revealed that both exist in at least two very defined conformational states: a “closed” cytosolic state, where the cargo-binding sites are buried within the complex, and an “open” state, where the same sites are available to bind cargo (7, 8, 10, 34, 35). Binding of Arf1 to AP-1 or PI(4,5)P₂ in case of AP-2 induces a conformational change in the complexes that enables them to bind cargo molecules carrying a conserved acidic di-Leucine or a Tyrosine-based motif, as for all three AP complexes in yeast (8, 34). Additional conformational states and intermediates have been reported for both, mammalian AP-1 and AP-2 complex. AP-1, for example, can be hijacked by the human immunodeficiency virus-1 (HIV-1) proteins viral protein u (Vpu) and negative factor (Nef), resulting in a hyper-open conformation of AP-1 (36, 37).An emerging model over the past years has suggested that APs have several binding sites that allow for the stabilization of membrane binding and the open conformation of the complexes, but there are initial interactions required that dictate their recruitment to the target membrane. Although these interaction sites for mammalian AP-1 and AP-2 have been identified in great detail based on interaction analyses and structural studies (8, 10, 11, 35, 36, 38, 39), structural data for AP-3 is largely missing. The C-terminal part of the μ-subunit of mammalian AP-3 has been crystallized together with a Yxxφ motif-containing a cargo peptide, which revealed a similar fold and cargo-binding site as shown for AP-1 and AP-2 (14). However, positively charged binding surfaces required for PIP-interaction were not well conserved. Although the “trunk” segment of AP-1 and AP-2 is known quite well by now, information on hinge and ear domains in context of these complexes is largely missing. Crystal structures of the isolated ear domains of α-, γ- and β2-adaptin have been published (40, 41, 42), and a study on mammalian AP-3 suggested a direct interaction between δ-ear and δ3 that interfered with Arf1-binding (43). Furthermore, during tethering of AP-3 vesicles with the yeast vacuole, the δ−subunit Apl5 of the yeast AP-3 complex binds to the Vps41 subunit of the HOPS complex as a prerequisite of fusion (18, 19, 21, 22).In this study, we applied single particle electron cryo-microscopy (cryo-EM) to analyze the purified full-length AP-3 complex from yeast and unraveled the factors required for AP-3 recruitment to membranes by biochemical reconstitution. Our data reveal that a surprisingly flexible AP-3 complex requires a combination of cargo, PI4P, and Arf1 for membrane binding, which explains its function in selective cargo sorting at the Golgi.     

What Use Is Population Genetics?

The Genetic Society of America’s Thomas Hunt Morgan Medal is awarded to an individual GSA member for lifetime achievement in the field of genetics. For over 40 years, 2015 recipient Brian Charlesworth has been a leader in both theoretical and empirical evolutionary genetics, making substantial contributions to our understanding of how evolution acts on genetic variation. Some of the areas in which Charlesworth’s research has been most influential are the evolution of sex chromosomes, transposable elements, deleterious mutations, sexual reproduction, and life history. He also developed the influential theory of background selection, whereby the recurrent elimination of deleterious mutations reduces variation at linked sites, providing a general explanation for the correlation between recombination rate and genetic variation.Open in a separate windowI am grateful to the Genetics Society of America for honoring me with the Thomas Hunt Morgan Medal and for inviting me to contribute this essay. I have spent nearly 50 years doing research in population genetics. This branch of genetics uses knowledge of the rules of inheritance to predict how the genetic composition of a population will change under the forces of evolution and compares the predictions to relevant data. As our knowledge of how genomes are organized and function has increased, so has the range of problems confronted by population geneticists. We are, however, a relatively small part of the genetics community, and sometimes it seems that our field is regarded as less important than those branches of genetics concerned with the properties of cells and individual organisms.I will take this opportunity to explain why I believe that population genetics is useful to a broad range of biologists. The fundamental importance of population genetics is the basic insights it provides into the mechanisms of evolution, some of which are far from intuitively obvious. Many of these insights came from the work of the first generation of population geneticists, notably Fisher, Haldane, and Wright. Their mathematical models showed that, contrary to what was believed by the majority of biologists in the 1920s, natural selection operating on Mendelian variation can cause evolutionary change at rates sufficient to explain historical patterns of evolution. This led to the modern synthesis of evolution (Provine 1971). No one can claim to understand how evolution works without some basic understanding of classical population genetics; those who do run the risk of making mistakes such as asserting that rapid evolutionary change is most likely to occur in small founder populations (Mayr 1954).

As our knowledge of how genomes are organized and function has increased, so has the range of problems confronted by population geneticists. We are, however, a relatively small part of the genetics community, and sometimes it seems that our field is regarded as less important than those branches of genetics concerned with the properties of cells and individual organisms.—B.C.

The modern synthesis is getting on for 80 years old, so this argument will probably not convince skeptical molecular geneticists that population genetics has a lot to offer the modern biologist. I provide two examples of the useful role that population genetic studies can play. First, one of the most notable discoveries of the past 40 years was the finding that the genomes of most species contain families of transposable elements (TEs) with the capacity to make new copies that insert elsewhere in the genome (Shapiro 1983). This led to two schools of thought about why they are present in the genome. One claimed that TEs are maintained because they confer benefits on the host by producing adaptively useful mutations (Syvanen 1984); the other believed that they are parasites, maintained by their ability to replicate within the genome despite potentially deleterious fitness effects of TE insertions (Doolittle and Sapienza 1980; Orgel and Crick 1980).The second hypothesis can be tested by comparing population genetic predictions with the results of TE surveys within populations. In the early 1980s, Chuck Langley, myself and several collaborators tried to do just this, using populations of Drosophila melanogaster (Charlesworth and Langley 1989). The models predicted that most Drosophila TEs should be found at low population frequencies at their insertion sites. This is so because D. melanogaster populations have large effective sizes (N_e). N_e is essentially the number of individuals that genetically contribute to the next generation. Large N_e means that a very small selection pressure can keep deleterious elements at low frequencies. This is a consequence of one of the most important findings of classical population genetics—the fate of a variant in a population is the product of N_e and the strength of selection (Fisher 1930; Kimura 1962). If, for example, N_e is 1000, a mutation that reduces fitness relative to wild type by 0.001 will be eliminated from the population with near certainty.Using the crude tools then available (restriction mapping of cloned genomic regions and in situ hybridization of labeled TE probes to polytene chromosomes), we found that nearly all TEs are indeed present at low frequencies in the population (Charlesworth and Langley 1989). Most of the exceptions to this rule were found in genomic regions in which little crossing over occurs (Maside et al. 2005). This is consistent with Chuck’s proposal that a major contributor to the removal of TEs from the population is selection against aneuploid progeny created by crossing over among homologous TEs at different locations in the genome (Langley et al. 1988). It is now a familiar finding that nonrecombining genomes or genomic regions tend to be full of TEs and other kinds of repetitive sequences; the population genetic reasons for this, discussed by Charlesworth et al. (1994), are perhaps not so familiar.Modern genomic methods provide much more powerful means for identifying TE insertions. Recent population surveys using these methods have confirmed the older findings: most TEs in Drosophila are present at low frequencies, and there is statistical evidence for selection against insertions (Barron et al. 2014). This is consistent with the existence of elaborate molecular mechanisms for repressing TE activity, such as the Piwi-interacting RNA (piRNA) pathway of animals (Senti and Brennecke 2010); there would be no reason to evolve such mechanisms if TEs were harmless. In a few cases, TEs have swept to high frequencies or fixation, and there is convincing evidence that at least some of these events are associated with increased fitness caused by the TE insertions themselves (Barron et al. 2014). These cases do not contradict the intragenomic parasite hypothesis for the maintenance of TEs; favorable mutations induced by TEs are too rare to outweigh the elimination of deleterious insertions unless new insertions continually replace those that are lost.

From the theory of aging, to the degeneration of Y chromosomes, to the dynamics of transposable elements, our understanding of the genetic basis of evolution is deeper and richer as a result of Charlesworth’s many contributions to the field. —Charles Langley, University of California, Davis

My other example is a population genetics discovery about a fundamental biological process: the PRDM9 protein involved in establishing recombination hot spots in humans. This was enabled by the revolution in population genetics brought about by coalescence theory (Hudson 1990), which is a powerful tool for looking at the statistical properties of a sample from a population under the hypothesis of selective neutrality. The basic idea is simple: if we sample two homologous, nonrecombining haploid genomes (e.g., mitochondrial DNA) from a large population, there is a probability of 1/(2N_e) that they are derived from the same parental genome in the preceding generation; i.e., they coalesce (N_e is the effective population size for the genome region in question). If they fail to coalesce in that generation, there is a probability of 1/(2N_e) that they coalesce one generation further back, and so on. If n genomes are sampled, there is a bifurcating tree connecting them back to their common ancestor. The size and shape of this tree are highly random, so genetically independent components of the genome experience different trees, even if they share the same N_e. The properties of sequence variability in the sample can be modeled by throwing mutations at random onto the tree (Hudson 1990).Recombination causes different sites in the genome to experience different trees, but closely linked sites have much more similar trees than independent sites. At the level of sequence variability, close linkage results in nonrandom associations between neutral variants—linkage disequilibrium (LD). The extent of LD among neutral variants at different sites is determined by the product of N_e and the frequency of recombination between them c (Ohta and Kimura 1971; McVean 2002). Richard Hudson proposed a statistical method for estimating N_ec from data on variants at multiple sites across the genome (Hudson 2001) that was implemented in a widely used computer program LDhat by Gil McVean and colleagues (McVean et al. 2002). Applications to large data sets on human sequence variability showed that the genome is full of recombination hot spots and cold spots, consistent with previous molecular genetic studies of specific loci (Myers et al. 2005). Most recombination occurs in hot spots and very little in between them, accounting for the fact that there is almost complete LD over tens or even hundreds of kilobases in humans. The identification of a large number of hot spots led to the discovery of a sequence motif bound by a zinc finger protein, PRDM9, at about the same time that mouse geneticists also discovered that PRDM9 promotes recombination (McVean and Myers 2010; Baudat et al. 2014). These discoveries have led to many interesting observations, such as associations between PRDM9 variants in humans and individual variation in recombination rates, generating an ongoing research program of great scientific interest (Baudat et al. 2014).With the ever-increasing use of genomic data, I am confident that many more such fruitful interactions between molecular and population genetics will take place. A take-home message is that more needs to be done to integrate training in population, molecular, and computational approaches to provide the next generation of researchers with the broad range of knowledge they will need.     

Extracellular DNA Is Required for Root Tip Resistance to Fungal Infection

Plant defense involves a complex array of biochemical interactions, many of which occur in the extracellular environment. The apical 1- to 2-mm root tip housing apical and root cap meristems is resistant to infection by most pathogens, so growth and gravity sensing often proceed normally even when other sites on the root are invaded. The mechanism of this resistance is unknown but appears to involve a mucilaginous matrix or “slime” composed of proteins, polysaccharides, and detached living cells called “border cells.” Here, we report that extracellular DNA (exDNA) is a component of root cap slime and that exDNA degradation during inoculation by a fungal pathogen results in loss of root tip resistance to infection. Most root tips (>95%) escape infection even when immersed in inoculum from the root-rotting pathogen Nectria haematococca. By contrast, 100% of inoculated root tips treated with DNase I developed necrosis. Treatment with BAL31, an exonuclease that digests DNA more slowly than DNase I, also resulted in increased root tip infection, but the onset of infection was delayed. Control root tips or fungal spores treated with nuclease alone exhibited normal morphology and growth. Pea (Pisum sativum) root tips incubated with [³²P]dCTP during a 1-h period when no cell death occurs yielded root cap slime containing ³²P-labeled exDNA. Our results suggest that exDNA is a previously unrecognized component of plant defense, an observation that is in accordance with the recent discovery that exDNA from white blood cells plays a key role in the vertebrate immune response against microbial pathogens.Root diseases caused by soil-borne plant pathogens are a perennial source of crop loss worldwide (Bruehl, 1986; Curl and Truelove, 1986). These diseases are of increasing concern, as pesticides like methyl bromide are removed from the market due to environmental concerns (Gilreath et al., 2005). One possible alternative means of crop protection is to exploit natural mechanisms of root disease resistance (Nelson, 1990; Goswami and Punja, 2008; Shittu et al., 2009). Direct observation of root systems under diverse conditions has revealed that root tips, in general, are resistant to infection even when lesions are initiated elsewhere on the same plant root (Foster et al., 1983; Bruehl, 1986; Curl and Truelove, 1986; Smith et al., 1992; Gunawardena et al., 2005; Wen et al., 2007). This form of disease resistance is important for crop production because root growth and its directional movement in response to gravity, water, and other signals can proceed normally as long as the root tip is not invaded. The 1- to 2-mm apical region of roots houses the root meristems required for root growth and cap development, and when infection does occur, root development ceases irreversibly within a few hours even in the absence of severe necrosis (Gunawardena and Hawes, 2002). Mechanisms underlying root tip resistance to infection are unclear, but the phenomenon appears to involve root cap “slime,” a mucilaginous matrix produced by the root cap (Morré et al., 1967; Rougier et al., 1979; Foster, 1982; Chaboud, 1983; Guinel and McCully, 1986; Moody et al., 1988; Knee et al., 2001; Barlow, 2003; Iijima et al., 2008). Within the root cap slime of cereals, legumes, and most other crop species are specialized populations of living cells called root “border cells” (Supplemental Fig. S1; Hawes et al., 2000). Border cell numbers increase in response to pathogens and toxins such as aluminum, and the cell populations maintain a high rate of metabolic activity even after detachment from the root cap periphery (Brigham et al., 1995; Miyasaka and Hawes, 2000).As border cells detach from roots of cereals and legumes, a complex of more than 100 proteins, termed the root cap secretome, is synthesized and exported from living cells into the matrix ensheathing the root tip (Brigham et al., 1995). The profile of secreted proteins changes in response to challenge with soil-borne bacteria (De-la-Peña et al., 2008). In pea (Pisum sativum), root tip resistance to infection is abolished in response to proteolytic degradation of the root cap secretome (Wen et al., 2007). In addition to an array of antimicrobial enzymes and other proteins known to be components of the extracellular matrix and apoplast of higher plants, the DNA-binding protein histone H4 unexpectedly was found to be present among the secreted proteins (Wen et al., 2007). One explanation for the presence of histone is global leakage of material from disrupted nuclei in dead cells, but no cell death occurs during delivery of the secretome (Brigham et al., 1995; Wen et al., 2007). An alternative explanation for the presence of a secreted DNA-binding protein is that extracellular DNA (exDNA) also is present in root cap slime.exDNA has long been known to be a component of slimy biological matrices ranging from purulent localized human infections to bacterial capsules, biofilms, and snail exudate (Sherry and Goeller, 1950; Leuchtenberger and Schrader, 1952; Braun and Whallon, 1954; Smithies and Gibbons, 1955; Catlin, 1956; Fahy et al., 1993; Allesen-Holm et al., 2006; Spoering and Gilmore, 2006; Qin et al., 2007; Izano et al., 2008). Specialized white blood cells in humans and other species including fish recently have been shown to deploy a complex neutrophil extracellular “trap” (NET), composed of DNA and a collection of enzymes, in response to infection (Brinkmann et al., 2004; Brinkmann and Zychlinsky, 2007; Palić et al., 2007; Wartha et al., 2007; Yousefi et al., 2008). NETs appear to kill bacterial, fungal, and protozoan pathogens by localizing them within a matrix of antimicrobial peptides and proteins (Urban et al., 2006; Wartha et al., 2007; Guimaraes-Costa et al., 2009). Several extracellular peptides and proteins implicated in neutrophil function, including histone, also are present within the pea root cap secretome (Wen et al., 2007). exDNA linked with extracellular histone is a structural component of NETs, and treatment with DNase destroys NET integrity and function (Wartha et al., 2007). Moreover, human pathogens including group A Streptococcus and Streptococcus pneumoniae release extracellular DNase (Sherry and Goeller, 1950). When these activities are eliminated by mutagenesis of the encoding genes, bacteria lose their normal ability to escape the NET and multiply at the site of infection (Sumby et al., 2005; Buchanan et al., 2006). Here, we report that, in addition to histone and other secretome proteins, exDNA also is a component of root cap slime. When this exDNA is digested enzymatically, root tip resistance to infection is abolished.     

The nuclease activity of DNA2 promotes exonuclease 1–independent mismatch repair

The DNA mismatch repair (MMR) system is a major DNA repair system that corrects DNA replication errors. In eukaryotes, the MMR system functions via mechanisms both dependent on and independent of exonuclease 1 (EXO1), an enzyme that has multiple roles in DNA metabolism. Although the mechanism of EXO1-dependent MMR is well understood, less is known about EXO1-independent MMR. Here, we provide genetic and biochemical evidence that the DNA2 nuclease/helicase has a role in EXO1-independent MMR. Biochemical reactions reconstituted with purified human proteins demonstrated that the nuclease activity of DNA2 promotes an EXO1-independent MMR reaction via a mismatch excision-independent mechanism that involves DNA polymerase δ. We show that DNA polymerase ε is not able to replace DNA polymerase δ in the DNA2-promoted MMR reaction. Unlike its nuclease activity, the helicase activity of DNA2 is dispensable for the ability of the protein to enhance the MMR reaction. Further examination established that DNA2 acts in the EXO1-independent MMR reaction by increasing the strand-displacement activity of DNA polymerase δ. These data reveal a mechanism for EXO1-independent mismatch repair.

The mismatch repair (MMR) system has been conserved from bacteria to humans (1, 2). It promotes genome stability by suppressing spontaneous and DNA damage-induced mutations (1, 3, 4, 5, 6, 7, 8, 9, 10, 11). The key function of the MMR system is the correction of DNA replication errors that escape the proofreading activities of replicative DNA polymerases (1, 4, 5, 6, 7, 8, 9, 10, 12). In addition, the MMR system removes mismatches formed during strand exchange in homologous recombination, suppresses homeologous recombination, initiates apoptosis in response to irreparable DNA damage caused by several anticancer drugs, and contributes to instability of triplet repeats and alternative DNA structures (1, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18). The principal components of the eukaryotic MMR system are MutSα (MSH2-MSH6 heterodimer), MutLα (MLH1-PMS2 heterodimer in humans and Mlh1-Pms1 heterodimer in yeast), MutSβ (MSH2-MSH3 heterodimer), proliferating cell nuclear antigen (PCNA), replication factor C (RFC), exonuclease 1 (EXO1), RPA, and DNA polymerase δ (Pol δ). Loss-of-function mutations in the MSH2, MLH1, MSH6, and PMS2 genes of the human MMR system cause Lynch and Turcot syndromes, and hypermethylation of the MLH1 promoter is responsible for ∼15% of sporadic cancers in several organs (19, 20). MMR deficiency leads to cancer initiation and progression via a multistage process that involves the inactivation of tumor suppressor genes and action of oncogenes (21).MMR occurs behind the replication fork (22, 23) and is a major determinant of the replication fidelity (24). The correction of DNA replication errors by the MMR system increases the replication fidelity by ∼100 fold (25). Strand breaks in leading and lagging strands as well as ribonucleotides in leading strands serve as signals that direct the eukaryotic MMR system to remove DNA replication errors (26, 27, 28, 29, 30). MMR is more efficient on the lagging than the leading strand (31). The substrates for MMR are all six base–base mismatches and 1 to 13-nt insertion/deletion loops (25, 32, 33, 34). Eukaryotic MMR commences with recognition of the mismatch by MutSα or MutSβ (32, 34, 35, 36). MutSα is the primary mismatch-recognition factor that recognizes both base–base mismatches and small insertion/deletion loops whereas MutSβ recognizes small insertion/deletion loops (32, 34, 35, 36, 37). After recognizing the mismatch, MutSα or MutSβ cooperates with RFC-loaded PCNA to activate MutLα endonuclease (38, 39, 40, 41, 42, 43). The activated MutLα endonuclease incises the discontinuous daughter strand 5′ and 3′ to the mismatch. A 5'' strand break formed by MutLα endonuclease is utilized by EXO1 to enter the DNA and excise a discontinuous strand portion encompassing the mismatch in a 5''→3′ excision reaction stimulated by MutSα/MutSβ (38, 44, 45). The generated gap is filled in by the Pol δ holoenzyme, and the nick is ligated by a DNA ligase (44, 46, 47). DNA polymerase ε (Pol ε) can substitute for Pol δ in the EXO1-dependent MMR reaction, but its activity in this reaction is much lower than that of Pol δ (48). Although MutLα endonuclease is essential for MMR in vivo, 5′ nick-dependent MMR reactions reconstituted in the presence of EXO1 are MutLα-independent (44, 47, 49).EXO1 deficiency in humans does not seem to cause significant cancer predisposition (19). Nevertheless, it is known that Exo1^-/- mice are susceptible to the development of lymphomas (50). Genetic studies in yeast and mice demonstrated that EXO1 inactivation causes only a modest defect in MMR (50, 51, 52, 53). In agreement with these genetic studies, a defined human EXO1-independent MMR reaction that depends on the strand-displacement DNA synthesis activity of Pol δ holoenzyme to remove the mismatch was reconstituted (54). Furthermore, an EXO1-independent MMR reaction that occurred in a mammalian cell extract system without the formation of a gapped excision intermediate was observed (54). Together, these findings implicated the strand-displacement activity of Pol δ holoenzyme in EXO1-independent MMR.In this study, we investigated DNA2 in the context of MMR. DNA2 is an essential multifunctional protein that has nuclease, ATPase, and 5''→3′ helicase activities (55, 56, 57). Previous research ascertained that DNA2 removes long flaps during Okazaki fragment maturation (58, 59, 60), participates in the resection step of double-strand break repair (61, 62, 63), initiates the replication checkpoint (64), and suppresses the expansions of GAA repeats (65). We have found in vivo and in vitro evidence that DNA2 promotes EXO1-independent MMR. Our data have indicated that the nuclease activity of DNA2 enhances the strand-displacement activity of Pol δ holoenzyme in an EXO1-independent MMR reaction.