Abstract
This article reviews the production of different phenotypes from the same genotype in the same environment by stochastic cellular events, nonlinear mechanisms during patterning and morphogenesis, and probabilistic self-reinforcing circuitries in the adult life. These aspects of phenotypic variation are summarized under the term ‘stochastic developmental variation’ (SDV) in the following. In the past, SDV has been viewed primarily as a nuisance, impairing laboratory experiments, pharmaceutical testing, and true-to-type breeding. This article also emphasizes the positive biological effects of SDV and discusses implications for genotype-to-phenotype mapping, biological individuation, ecology, evolution, and applied biology. There is strong evidence from experiments with genetically identical organisms performed in narrowly standardized laboratory set-ups that SDV is a source of phenotypic variation in its own right aside from genetic variation and environmental variation. It is obviously mediated by molecular and higher-order epigenetic mechanisms. Comparison of SDV in animals, plants, fungi, protists, bacteria, archaeans, and viruses suggests that it is a ubiquitous and phylogenetically old phenomenon. In animals, it is usually smallest for morphometric traits and highest for life history traits and behaviour. SDV is thought to contribute to phenotypic diversity in all populations but is particularly relevant for asexually reproducing and genetically impoverished populations, where it generates individuality despite genetic uniformity. In each generation, SDV produces a range of phenotypes around a well-adapted target phenotype, which is interpreted as a bet-hedging strategy to cope with the unpredictability of dynamic environments. At least some manifestations of SDV are heritable, adaptable, selectable, and evolvable, and therefore, SDV may be seen as a hitherto overlooked evolution factor. SDV is also relevant for husbandry, agriculture, and medicine because most pathogens are asexuals that exploit this third source of phenotypic variation to modify infectivity and resistance to antibiotics. Since SDV affects all types of organisms and almost all aspects of life, it urgently requires more intense research and a better integration into biological thinking.
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1 Introduction
The phenotype is of outstanding importance in biology because it expresses individuality, interacts with the environment, and serves as target of natural selection (Rollo 1995; Schlichting and Pigliucci 1998; West-Eberhard 2003; Piersma and van Gils 2011; Jablonka and Lamb 2014). Despite intense research for more than a century, we are far from fully understanding how phenotypes are produced from genetic templates and how phenotypic variation is generated (Nanjundiah and Newman 2009; Pigliucci 2010; Hallgrímsson and Hall 2011a; Sozzani and Benfey 2011; Landry and Rifkin 2012).
It is generally accepted that phenotypic diversity within a population or species is produced by differences in alleles and differences in environmental inputs that modify gene expression (Falconer and Mackay 1996; Jaenisch and Bird 2003; Nanjundiah 2003; Hallgrímsson and Hall 2005; Charlesworth and Charlesworth 2010). According to this standard concept, genetically identical organisms kept in the same narrowly controlled environment should be phenotypically identical, which is, however, not the case at all (Gärtner 1990; Veitia 2005; Peaston and Whitelaw 2006; Vogt et al. 2008). In practically all cases investigated, there was a residual variation aside of genetic and environmental variation, sometimes exceeding half of the total phenotypic variation (Falconer and Mackay 1996).
The idea of the existence of a third source of phenotypic variation, which is based on developmental stochasticity, is more than a century old (Warren 1902). However, in contrast to genetic variation and environmental variation, stochastic developmental variation (SDV) was mostly regarded as an insignificant background phenomenon resulting from the imperfection of biological processes (Charlesworth and Charlesworth 2010). Authors who recognized its biological relevance often treated it together with the environmental proportion of phenotypic variation on a common conceptual footing under the term phenotypic plasticity sensu lato (Nanjundiah 2003; West-Eberhard 2003). SDV can be best determined in laboratory experiments with clonal organisms, in which genetic variation and environmental variation can be kept close to zero. This article uses only such data with the exception of some information on highly variable traits in human monozygotic twins that are produced in the relatively homogeneous environment of the uterus.
This review is an attempt to conceptualize SDV across the entire spectrum of organisms and to elaborate its biological relevance. Special emphasis is given to animals, the most complex organisms with the widest spectrum of SDV generators. The article starts with the definition of some key terms and continues with a historical account of research on SDV to demonstrate its long tradition. It then surveys SDV in major clades of organisms to examine its distribution across the tree of life. Thereafter, a detailed phenomenology of SDV is presented using the animals as an exemplary higher taxon. The following sections deal with the sources and limitations of SDV, its mediation by epigenetic mechanisms, and its implications for genotype-to-phenotype mapping, individuality, applied biology, ecology, and evolution. The last section addresses problems and open questions and makes some suggestions for future research.
2 Definitions
The terms and concepts required to develop the ideas of this article are not consistently used in literature. To avoid misunderstandings, I will here explain how they are applied in the following.
Clonal organisms are at the centre of this work. A clone is an assemblage of individuals genetically identical by descent that originates from asexual reproduction, inbreeding, polyembryony, and artificial cloning (Hughes 1989; De Kroon and van Groenendael 1997; Spratt 2004). Asexual reproduction refers to reproduction without meiosis and fertilization with the offspring being copies of their parent. In animals, it includes budding, fragmentation and apomictic parthenogenesis, in plants vegetative reproduction and apomixis, and in bacteria binary fission, budding, fragmentation and spore formation. Inbreeding is the continued breeding of closely related individuals that finally results in genetic identity. Polyembryony is the development of two or more genetically identical embryos from a fertilized egg. Artificial clones are produced either by embryo splitting or by somatic cell nuclear transfer.
The genotype is the genetic make-up of an organism or the entire set of genes it carries (Johannsen 1913). In studies on genotype–phenotype relationships, notably in those with sexually reproducing species, the term is often used in a broader sense characterizing the genetic make-up of a population or even species. Such population genotypes are in fact composed of many different DNA-sequences. In this article, I use the term genotype only in its narrow sense applying to an individual DNA-sequence and its copies. The term phenotype refers to the quality or quantity of a trait that can be compared among population members (Mahner and Kary 1997). It mostly refers to distinct morphological, biochemical, physiological, behavioural, or life history traits but can also concern features that characterize specimens as individuals such as physiognomy and coat coloration.
The phenotypic variation of a population or species consists of three proportions: genetic variation, environmental variation, and stochastic developmental variation (Falconer and Mackay 1996; Finch and Kirkwood 2000; Nanjundiah 2003). Genetic variation comes from changes in the DNA sequence caused by random mutation, transposon shifts, and genetic recombination (Falconer and Mackay 1996). Environmental variation is the result of different inputs of the external environment. However, many authors use this term for all sources of non-genetic phenotypic variation, including influences of the internal milieu of organisms. Since the latter is the realm of stochastic developmental variation, I restrict the term environment to the external conditions that an organism experiences such as habitat, temperature, water parameters, and food.
In animals, the residual proportion of phenotypic variation that is neither attributable to genetic variation nor environmental variation was called developmental noise (Waddington 1957), intangible variation (Falconer and Mackay 1996), third component (Gärtner 1990), random noise (Lewontin 2000), intrinsic chance variation (Finch and Kirkwood 2000), or developmental variation (Vogt et al. 2008). Sometimes, it was also referred to as developmental instability (Klingenberg 2003) but this term often includes genetic and environmental variations as well. Authors working on stochastic processes in unicellular organisms use a variety of different terms, depending on their field of interest or angle of view. Examples are gene expression noise (Raj and van Oudenaarden 2008), cellular noise (Johnston et al. 2012), and phenotypic noise (Freed et al. 2008). The need for an umbrella term, which unifies all stochastic cellular and developmental processes that contribute to phenotypic variation, prompted me to introduce here the term stochastic developmental variation (SDV) in analogy to genetic variation and environmental variation.
The term was first used by Orias and Bradshaw (1992). The word stochastic implies a probabilistic nature of the processes involved lacking any predictable order or plan (Meyer and Roeder 2014). Development is the progressive change in shape, size, and function during the life of an organism by which its genetic potentials are translated into phenotypes. It covers cleavage, cell differentiation, patterning, and morphogenesis but also tissue regeneration and life history attributes in the adult life. The degree to which the adult life contributes to SDV is dependent on the growth format of an organism, being higher in indeterminately than in determinately growing species.
SDV is often confused with or subsumed under the term phenotypic plasticity, which is a hot topic in ecology and evolutionary biology (Pigliucci 2001; Nanjundiah 2003; Fusco and Minelli 2010). In this article, I follow the distinction between the two made by Scheiner (1993): SDV is the production of different phenotypes from a single genotype in a single environment, whereas phenotypic plasticity is the production of different phenotypes from a single genotype in different environments. Both have in common that they are mediated by epigenetic mechanisms (Jaenisch and Bird 2003).
The concept of epigenetics was originally introduced by Waddington (1957) to describe the variety of developmental phenomena above the level of the genome in order to link the genotype to the phenotype. Epigenetic mechanisms can be subdivided into molecular and higher-level ones (Hallgrímsson and Hall 2011b). Molecular epigenetics refers to the study of stable alterations in gene expression by mechanisms other than changes in the underlying DNA sequence, for instance, DNA methylation and histone modification (Bird 2002; Lennartsson and Ekwall 2009). Unlike the genetic code, these marks can be erased and rewritten during the lifetime. Higher-level epigenetics includes chemical and mechanical cell-to-cell interactions, self-organization of tissues, and self-reinforcing circuitries including behaviour, metabolism, and neuroendocrine control (Newman and Müller 2005; Vogt et al. 2008; Hallgrímsson and Hall 2011b).
3 History of research on SDV
Research on the stochastic developmental proportion of phenotypic variation is almost as old as research on its genetic and environmental proportions. In 1899, Ernest Warren published a short note on the variation of morphometric traits in parthenogenetic broods of the water flea Daphnia magna (Warren 1899). Three years later, the same author presented a more detailed work on SDV in parthenogenetic aphis Longicaudus trirhodus, in which genetic and environmental variation was minimized by encaging batches on their own leaves (Warren 1902). In the same period of time, Karl Pearson and colleagues collected large amounts of data on the non-genetic and non-environmental variability of repeated traits in individual plants and fungi (Pearson et al. 1901). Five years later Raymond Pearl published on the variation of morphometric parameters in communally raised asexual flagellate Chilomonas paramecium (Pearl 1906).
Newman and Patterson (1909) were the first to use polyembryonic species for research on SDV. They determined differences in the number of scales in genetically identical quadruplets of nine-banded armadillos. The first discussion of SDV in a textbook also dates from 1909. Wilhelm Ludwig Johannsen argued in his book Elemente der exakten Erblichkeitslehre that genetically identical organisms can be phenotypically different even when environmental factors are kept as uniform as possible. He attributed such differences to random events, which may have disturbing or promoting effects on development (Johannsen 1913). Some years later, Sewall Wright used inbred Guinea pig families to analyse genetics of piebald spotting (Wright 1920). He found that variation of colour patterns is determined at about 3% by heredity, 5% by the environment, and 92% by ‘developmental irregularities’.
In 1930, Astauroff published a voluminous study on SDV in animals, which covers all aspects of SDV, namely, variation of traits among genetically identical group members raised in the same environment, variation between right and left body sides in bilaterally symmetrical animals, and variation between serially homologous body parts in segmented animals (Astauroff 1930). This paper also includes an extensive list of the earlier literature.
Between the 1930s and 1980s, the number of publications on SDV in animals and plants remained on a relatively low level. Noteworthy are the articles by Wright and Chase (1936), Grüneberg (1954), Reeve and Robertson (1954), Roy (1963), Storrs and Williams (1968), Macagno et al. (1973), Gärtner et al. (1976), and Spudich and Koshland (1976). Research on right–left differences of traits in bilaterally symmetrical organisms, later called fluctuating asymmetry, developed as a separate field, often interpreting asymmetry as the result of developmental stochasticity but also as the outcome of genetic disturbances and environmental stress (Van Valen 1962; Palmer and Strobeck 1986; Parsons 1992; Leamy and Klingenberg 2005; Graham et al. 2010).
SDV in bacteria was first demonstrated in the 1940s. Bigger (1944) reported on the occurrence of cells in clonal populations of Staphyllococcus that could withstand penicillin treatment. These persisters had not acquired antibiotics resistance by mutations and remained sensitive to penicillin after having established a new population. Delbrück (1945) observed broad variation of burst size of viruses from individual Escherichia coli cells that could not be accounted for by variation in the size of the bacteria. Benzer (1953) analysed synthesis of β-d-galactosidase in individual E. coli and demonstrated heterogeneity of enzyme production among clonal cells under identical conditions. Novick and Weiner (1957) revealed that isogenic E. coli can respond to an enzyme-inducing stimulus by an all-or-none response, and Cohn and Horibata (1959) demonstrated that both the formation and non-formation of enzyme could be passed on clonally to the descendants.
Modern research on SDV comes mainly from three directions: stochastic gene expression in unicellular organisms, somaclonal variation in plants, and research on phenotypic diversity in clonal animals kept under highly standardized conditions. Research on stochastic gene expression was initiated by McAdams and Arkin (1997), Thattai and van Oudenaarden (2001), and Elowitz et al. (2002), and resulted in hundreds of papers since then, using bacteria and yeast as model organisms. Somaclonal variation, the phenotypic variation of clonal plants raised in tissue culture, was introduced to science by Larkin and Scowcroft (1981). Due to its significance in applied plant biology, this phenomenon has yielded numerous articles as well. However, somaclonal variation is based on genetic and epigenetic mutations (Skirvin et al. 1994; Bairu et al. 2011; Miguel and Marum 2011), and only the latter aspect is of interest for this review. Research on SDV in animals has been revived by Gärtner’s paper on a third component causing random variability beside environment and genotype (Gärtner 1990). Since then, a few dozen articles and book chapters have addressed this topic. In 2012, the International Journal of Epidemiology has devoted a special volume to SDV and its potential implications for animals and man (vol. 41, issue 2).
Three important developments in biology boosted research on SDV in the last decade, the invention of new single-cell techniques (Spiller et al. 2010; Wang and Bodovitz 2010), the increased application of bioinformatics tools (O’Connor and Mundy 2009; Ning et al. 2014), and the progress of molecular epigenetics (Bird 2002; Laird 2010). Single-cell analysis facilitates investigation of non-genetic phenotypic variation in microbial clones, which was lost in earlier bulk measurements. Epigenetics research is expected to contribute significantly to understanding of the mechanisms that mediate SDV, and bioinformatics tools enable processing of huge amounts of genetic and epigenetic data.
In recent years, scientists have realized that SDV has not only disadvantages but also advantages. For instance, Huang (2009) discussed the importance of SDV for cell differentiation and argued that it is ‘more than just noise’. Forde (2009) examined the role of SDV in the development of root systems in plants and appraised it ‘good noise’. We demonstrated that SDV makes genetically identical animals morphologically and functionally distinct individuals (Vogt et al. 2008). Raj and van Oudenaarden (2008) interpreted stochastic gene expression in microorganisms as an important bet-hedging strategy against stress. Nanjundiah (2003) and Eldar and Elowitz (2010) emphasized its possible contribution to the evolution of microbes and multicellular organisms, and Patnaik (2006) pointed at the potential exploitability of SDV for biotechnology. Consideration of these positive biological aspects adds a new dimension to the discussion on SDV and gets it out of the shadows.
4 Occurrence of SDV across the tree of life
In the last chapter, I have cited some examples of SDV from distantly related taxa, which may suggest that SDV is a universal phenomenon. Here, this issue is examined in detail starting with the complex animals and ending with the simple viruses. For reasons of convenience, I use the widespread classification of the living world into Animalia, Plantae, Fungi, Protista, Bacteria, Archaea, and Viruses (Woese et al. 1990; Margulis and Schwartz 1997). Protista is a taxonomically abolished assemblage that includes unicellular organisms and some multicellular representatives with low tissue specialization (Adl et al. 2012). The viruses are polyphyletic as well and are interpreted either as the most primitive pre-cellular ‘organisms’ or as a form at the edge of life (Koonin 2012).
Although there are not too many studies on SDV in animals, they cover all major evolutionary lineages. Examples of SDV for morphological, biochemical, physiological, behavioural, and life history traits are available for the Porifera, the most basal branch of the Animalia, the diploblastic Cnidaria, the protostomian Bryozoa, Platyhelminthes, Rotifera, Nematoda, Mollusca, and Arthropoda, and the deuterostomian Ascidiacea and Vertebrata (table 1; figure 1A). In fungi, the closest relatives of the animals, SDV is documented for several species from the two main branches, the Ascomycota and Basidiomycota for gene expression, hyphal morphology and resistance to antimycotics. The species investigated include the model organism Saccharomyces cerevisiae, the food contaminant Aspergillus niger and the opportunistic pathogen Cryptococcus neoformans (table 1; figure 1B).
Examples of SDV from all kingdoms of life. (A) Variation of size in identically reared batchmates of apomictic parthenogenetic crayfish Procambarus fallax f. virginalis (from Vogt et al. 2008). (B) Variation in asexual formation of conidia (arrow) in fungus Aspergillus niger (from Dijksterhuis and Wösten 2013). (C) Variation of coloration in iterative flowers of orchid Dendrobium antennatum. The colour patterns of the labellum vary between flowers and between right and left sides in each flower (arrows) (photo: Rogier R van Vugt). (D) Variation of size and morphology of asexually produced amoebospores (arrows) released from the same spore receptacle (sr) in the protist Psorospermium haeckeli (from Vogt and Rug 1999). (E) Differential gene expression in bacterium Salmonella ser. Typhimurium indicated by green fluorescence protein (arrow). The cells were cultured from a single cell over six generations (from Ackermann 2013). (F) Variation of form and size in archaean Haloterrigena turkmenica (from Saunders et al. 2010). (G) Variation of form and length in Marburg virus (photo: Erskine L Palmer and Russell Regnery).
In plants, the morphologically most diverse organisms aside of animals, information on SDV comes from the morphology of leaves, branches and roots, the colour of flowers, fruits and leaves, some life history traits, and molecular parameters such as gene expression and epigenetic profiles (table 1; figure 1C). In table 1, I have listed examples for the monocotyledonous orders Asparagales and Zingiberales, and the dicotyledonous orders Brassicales, Lamiales, Solanales, Ericales, Fabales, Malvales, and Malpighiales. Included is also Arabidopsis thaliana, the most widely used model organism in plant biology.
In protists, data on SDV are published for morphometric parameters, cell fate determination, spore formation, and surface antigen expression in species from distantly related clades like Choanoflagellata, Mesomycetozoa, Amoebozoa, Cryptophyta, Ciliophora, Apicomplexa, Euglenozoa, and Chlorophyta (table 1; figure 1D). These examples include the free-living and colony-forming Salpingoeca rosetta, the malaria causing parasite Plasmodium falciparum, and the slime mould Dictyostelium discoideum, a popular model for cellular differentiation.
There is also sound evidence of SDV in bacteria from different orders concerning growth, gene expression, protein content, behaviour, virulence, and resistance to antibiotics (table 1; figure 1E). Examples are the model organisms Escherichia coli and Bacillus subtilis, the ubiquist Pseudomonas aeruginosa, and the pathogens Mycobacterium tuberculosis and Streptococcus pneumoniae. In the Archaea, there are examples on SDV for cell size, DNA content, and swimming behaviour in species from different orders (table 1; figure 1F). SDV has even been found in phage-λ and HIV viruses with respect to lysis–lysogeny switching and in rod-shaped baculoviruses and filiform Marburg virus with respect to form and length (table 1; figure 1G).
The occurrence of SDV in all kingdoms of life and all subclades and life forms investigated suggests that it is a universal biological phenomenon inherent in all living beings.
5 Phenomenology of SDV on the example of animals
This section examines the dimensions of SDV, using animals, the evolutionary lineage with the highest complexity of body plans, life styles, and behaviours, as an example. The focus is set on morphological, physiological, behavioural, and life history traits. SDV of qualitative traits is illustrated by photographs and SDV of quantitative traits by the coefficient of variation (CV). CV is the ratio of standard deviation to mean and allows comparison among data sets and species. Information on the phenomenology of SDV in plants is found in Roy (1963), Freeman et al. (1993) and Miguel and Marum (2011). Respective data for yeast, protists and bacteria were compiled by Raser and O’Shea (2005), Avery (2006), and Raj and van Oudenaarden (2008).
5.1 Animal models of SDV
The outcome of research and the resulting philosophy in a scientific discipline is much dependent on the features of the research models used. Therefore, I want to discuss here briefly the advantages and disadvantages of the common animal models of SDV. These are a dozen vertebrates and invertebrates, which differ in the way they produce genetically identical offspring, the degree to which their housing can be standardized, the quality and quantity of analysable traits, their life history features, and knowledge on genetics and epigenetics.
Common animal models of SDV are inbred strains of mouse Mus musculus, rat Rattus norvegicus, guinea pig Cavia porcellus and fruit fly Drosophila melanogaster, the selfing nematode Caenorhabditis elegans, polyembryonic nine-banded armadillo Dasypus novemcinctus, artificially produced clones of cattle Bos taurus and pig Sus scrofa domestica, and parthenogenetic lineages of pea aphid Acyrthosiphon pisum and water flea Daphnia pulex. Several of these species like mouse, rat, and fruit fly are long established as research models (Beck et al. 2000; Rubin and Lewis 2000) and manuals for their standardized housing and breeding are available.
Inbreeding, polyembryony, parthenogenesis, and artificial cloning are all suitable means to produce genetically identical research material but the outcome differs in detail (Bailey 1982; Hiendleder 2007; Avise 2008). Inbred lineages of sexually reproducing species are almost 100% genetically identical after more than 40 generations of full-sibling mating. They are homozygous for all autosomal loci and include males and females. In hermaphrodites, prolonged self-fertilization also results in near genetic identity. Monozygous twins and polyembryonic littermates differ from inbred lineages in as far as they are genetically identical among each other but different from their parents. Moreover, they are all of the same sex, either male or female. Artificially manufactured clonemates are genetically identical when embryo splitting is applied but usually differ in mitochondrial DNA when produced by somatic cell nuclear transfer. In obligate apomictic parthenogenetic lineages, all members are genetically identical females.
Experiments on SDV also require maximum standardization of the environmental parameters. This is easier achieved with small animals like fruit flies, water fleas, and aphids than with large animals like pigs and cattle. The small-sized models have the additional advantage of cheaper mass culture and a much shorter generation time. However, they are unsuitable for individual biochemical and physiological measurements and individual analyses of transcriptome, proteome, and methylome. Mouse and rats have appropriate sizes for both, mass culture under highly standardized conditions and individual biochemical analyses.
The common SDV models also show considerable differences in the quality and quantity of analysable characters. For instance, variation in coloration and spotting is the rule in some guinea pig and cattle lineages but is rare or absent in the other species. Armadillos, insects, and water fleas possess rigid external structures such as scales and sensory setae that allow exact measurement and counting. Caenorhabditis elegans, in contrast, has very few morphological traits suitable for measurement. The life history features vary broadly as well. For instance, Caenorhabditis elegans has a generation time of 3–4 days and a life span of 2–3 weeks, which is in sharp contrast to cattle that has generation times of 2 years and average life spans of 15 years. The number of offspring per clutch is usually 1 in cattle, 3–8 in mouse and up to 30 in water flea.
There are also considerable differences with respect to development and growth format. The mammals and daphnids develop directly, whereas insects and the nematode develop indirectly through larval stages. Caenorhabditis elegans is exceptional because it has a constant cell number and an invariant cell fate pattern. The insect and mammalian models show determinate growth, i.e. they stop growth at the beginning of the reproductive life period, whereas daphnids grow indeterminately and change morphological traits until the end of life. The early life stages of the mammalian species are not directly observable because they develop in the uterus, whereas in the invertebrate models all life stages are accessible for observation, sampling, and manipulation.
Knowledge on genetics is quite good in most of the models. Mouse, rat, guinea pig, and fruit fly are already used for more than a century to study genetic issues (Rubin and Lewis 2000; Paigen 2003). Whole genome sequences are available for mouse, rat, cattle, pig, guinea pig, nine-banded armadillo, fruit fly, nematode, pea aphid, and water flea. Methylation of the DNA, which seems to be a prime epigenetic mediator of SDV, is intensely investigated in the mammalian models and less intensely in aphids and daphnids (Walsh et al. 2010; Chen and Riggs 2011). In Drosophila and Caenorhabditis the DNA is unmethylated (Raddatz et al. 2013).
A few years ago, I introduced the obligate parthenogenetic marbled crayfish Procambarus fallax f. virginalis (figures 1A and 4D) as a new model for research on SDV (Vogt 2008; Vogt et al. 2008). This crustacean meets very well the requirements for studying SDV in the laboratory, namely, genetic identity of all population members, housing in simple environments, and possession of numerous characters easy to analyse. It has an adult size of 4–12 cm, a generation time of about 6 months and a life span of 2–3 years. Marbled crayfish are indeterminately growing and can reproduce about seven times in their life having up to 400 siblings per clutch. The eggs, embryos and first three juvenile stages are brooded on the maternal pleopods (figure 4D). All life stages can be raised individually or communally in very simple laboratory settings and can be fed with a single pellet food (Tetra WaferMix) throughout life, enabling maximum standardization of the environmental conditions (Vogt 2008, 2011). The genome is already fully sequenced and a first draft of the genome assembly is available. The DNA is methylated in all life stages having a methylation level close to that of the mammalian models (Vogt et al. 2008, 2013).
5.2 Inter-individual SDV
This section gives an overview of the dimensions of SDV in genetically identical groups of animals raised under strictly controlled laboratory conditions. Included is also some information on highly variable traits in human monozygotic twins that arise during embryonic development. Most articles on inter-individual SDV in animals include information on only one or two traits. Broader spectra of traits, which allow the identification of differences between traits and trait categories, were analysed by Gärtner and colleagues, who investigated 25 quantitative traits in 58 inbred groups of rats each consisting of 20 specimens (Gärtner et al. 1976; Gärtner 1985, 1990). Likewise, Tordoff and colleagues examined nine biochemical and behavioural traits in 40 inbred groups of mice (Reed et al. 2007; Tordoff et al. 2007a, b). Storrs and Williams (1968) studied 20 morphological and biochemical traits in 16 armadillo quadruplets, and Friend and colleagues investigated 24 morphological, biochemical, and behavioural characters in two groups of cloned pigs (Archer et al. 2003a, b). We investigated 22 morphological, biochemical, behavioural, and life history traits in 28 batches of the parthenogenetic marbled crayfish (Vogt 2007, 2010; Vogt et al. 2008, 2009).
5.2.1 Morphological traits
The variation of qualitative morphological characters among identically reared isogenic animals is often seen at first glance. Examples are the horn form in cloned cattle (figure 2A), the hairiness in cloned pigs (figure 2B), and the arrangement of scales in the head shields of polyembryonic armadillo littermates (figure 2C). SDV can be extremely broad in some traits, for example, in fingerprints (figure 3A), palm prints, irises, and retinas of humans (Daugman and Downing 2001; Jain et al. 2002; Kong et al. 2006), and in noseprints of cattle (figure 3B) (Yang et al. 2012). These traits become diverse in the relatively uniform environment of the uterus and persist largely unchanged throughout life. In contrast to the DNA profiles, these traits differ among clonemates and are therefore used for individual authentication of humans and cattle.
SDV of morphological traits in animals. (A) Variation of horn pattern in cloned Hanwoo cattle. Clone 1 (C1) has a horn similar to the nuclear donor mother (M) but different to its clonemate (C2) (from Yang et al. 2012). (B) Variation of hair pattern in cloned Duroc pigs. The left specimen has longer and sparser hair than its clonemate on the right (from Archer et al. 2003a). (C) Differences in patterns of skeleton shields in a quadruplet of polyembryonic nine-banded armadillo. Red circles show variation in head shields (h) of two littermates. The banded shield (b) is particularly suitable for analysis of SDV due to highly regular arrangement of scales as shown in the right panel. Inscription gives range (r) and coefficient of variation (CV) of the number of scales (S) in the banded shields of one quadruplet (left photo: Brian Bagatto; right photo: David G. Haskell; data from Newman 1913). (D) Variation of metric and meristic traits in marbled crayfish. Analysed were carapace length (white bar), total length (black bar), number of aesthetascs on 1st antennae (arrowhead) and number of corrugated setae on pereopods 1 to 3 (arrows). Left panels show scanning electron micrographs of aesthetascs and corrugated setae. Inscriptions give ranges and CVs of carapace length (CL), total number of aesthetascs (AE) and total number of corrugated setae (CS) for a batch of 11 stage-6 juveniles (from Vogt et al. 2008).
Examples of particularly variable morphological traits. (A) Fingerprints of corresponding index fingers of human monozygotic twins showing differences in pattern of ridges (left panels). The differences become particularly obvious in the minutiae patterns (right panels) extracted from the fingerprints for automatic authentication (from Jain et al. 2002). (B) Noseprints of cloned Hanwoo cattle showing striking differences between nuclear donor cow and clonemates 1 to 3 in comparable areas (red circles) (from Yang et al. 2012).
Coat coloration is another qualitative trait that can vary widely between identically raised clone members. For instance, inbred Avy mice show coat colours from pure yellow to agouti (figure 4A), and parthenogenetically produced batchmates of pea aphid show whitish to green coloration (figure 5A). Much more variable than the colour hue is colour patterning as shown for inbred guinea pigs (Chase 1939), cloned Holstein cattle (figure 4B), cloned cats (figure 4C), and parthenogenetic marbled crayfish (figure 4D). The marmoration motifs of the latter differ greatly between mother and offspring and among batchmates (figure 4D). Each of the many hundred specimens examined by us had a unique marmoration pattern that unambiguously identified the individuals, despite their genetic identity (Vogt et al. 2008).
SDV of coloration in animals. (A) Inbred Avy mouse littermates showing colour variation from pure yellow (left) to pseudoaguti (right) (from Cropley et al. 2010). (B) Monozygotic twins of Holstein cattle derived from a split embryo and their donor mother showing differences in spotting pattern (from Seidel et al. 2003; photo: John F. Hasler). (C) Nuclear donor mother (M) and cloned offspring (O) of cat showing marked differences in both colours and spotting patterns (from Shin et al. 2002; photos: Larry Wadsworth). (D) Parthenogenetic marbled crayfish showing variation in marmoration patterns. White frame indicates posteriolateral part of cephalothorax used for comparison of colour patterns. The four panels on the right show striking differences between a mother (M) and three adult offspring (O1–O3) of the same batch (from Vogt et al. 2008).
SDV of life history traits in animals. (A) Differences in coloration and size among parthenogenetically produced offspring of pea aphid (photo: Alex Wild). (B) Size differences between 27-weeks old cloned Duroc pigs originating from the same nuclear donor cell line and born to the same recipient mother. The pigs were kept communally and had unlimited access to food and water (from Archer et al. 2003a). (C) Variation in speed of development among 12 batchmates of parthenogenetic marbled crayfish raised individually in the same microplate. Shown is a 39-day period from 80% embryonic development to juvenile stage 5. Differences in development are small in the lecithotrophic period and increase markedly from stage 3, the first feeding stage (from Vogt et al. 2008). (D) Variation in body size and egg number (arrows) in highly synchronized parthenogenetic water fleas (photo: Winfried Lampert). (E) Variation of life span, body weight and reproduction frequency in eight communally reared batchmates of marbled crayfish (B1–B8). Life span is indicated by red columns, final weight by blue columns, frequency of reproduction by figures on red columns, and time points of egg laying by horizontal bars in red columns. Specimens indicated by asterisks were sacrificed for biochemical analysis; the other crayfish died a natural death (from Vogt 2015). (F) Variation of life span in wild-type (red) and long-lived age-1 (blue) strains of selfing nematode Caenorhabditis elegans (redrawn after Kirkwood and Finch 2002; photo: Jürgen Berger).
SDV of metric (measurable) and meristic (countable) morphological traits is mostly relatively small (table 2). For example, the CVs of body length in two clones of red-spotted cherry salmon Oncorhynchus masou macrostomus were 4.65% (n=24) and 5.00% (n=22) (Iguchi et al. 2001). The CVs of the number of scales in the banded region of polyembryonic armadillos (figure 2C) were even smaller varying only between 0.39% and 3% in 16 quadruplets (Storrs and Williams 1968). In batches of marbled crayfish juveniles, the CVs of carapace length, total length, and the numbers of olfactory and gustatory sense organs (figure 2D) varied between 2.73% and 18.57% but were mostly below 10% (Vogt et al. 2008; unpublished data).
Interestingly, in a given group of animals CVs for different morphological traits were usually not the same. For example, in a newborn armadillo quadruplet, CVs of the weights of brain, heart, and spleen were 5.52%, 14.65%, and 29.99%, respectively (table 2). Likewise, in 12 juvenile stage-3 batchmates of marbled crayfish CVs of carapace length, number of olfactory aesthetascs, and number of gustatory corrugated setae were 2.73%, 6.43%, and 3.72% (table 2).
5.2.2 Biochemical and physiological traits
SDVs of biochemical traits can either be small or high depending on trait and species. For example, the blood parameters serum protein, glucose and albumin in cloned pigs had CVs below 10% (table 2). Blood calcium, which is generally narrowly regulated, had a CV below 1%. In contrast, water content, fat content and ash had CVs between 10% and 20% in inbred mice (Dawson 1970). Higher CVs of 20% to 135% were determined for the hormones cortisol, growth hormone, triiodothyronine, thyroxine, insulin, and adrenalin in polyembryonic armadillos and cloned pigs and goats (table 2).
Growth differences among identically raised clonemates are often obvious at first glance as shown for parthenogenetic marbled crayfish (figure 1A), parthenogenetic pea aphids (figure 5A), and cloned pigs (figure 5B). Quantitatively, growth differences can be determined by comparison of size and weight at a given time, size and weight increment in a given period of time, and, in arthropods, the proportion of different instars at a given time. Examples of CVs of body weight are 5.72% in a newborn armadillo quadruplet and 30.91% in a group of 8 batchmates of marbled crayfish at day 152 of life (table 2). In male groups (n=10) of inbred mice from 40 strains, the CVs of daily weight gain varied between 0 and 200% (mean: 19.61%) (Tordoff et al. 2007a).
Considerable variation in growth was also noted when batchmates of marbled crayfish were individually raised in a 12-well microplate through the late embryonic and early juvenile stages (figure 5C). In the lecithotrophic embryos and juvenile stages 1 and 2, development was rather uniform, but from juvenile stage 3, the first feeding stage, the speed of development became increasingly diverse. An increase of SDV of growth with age was also observed in a group of 8 marbled crayfish, which had CVs of body weight of 10.29%, 14.37%, 20.98%, 30.91%, and 48.05% at days 71, 101, 143, 152, and 351 after hatching, respectively.
The semi-independency of traits with respect to SDV as observed for morphological characters is also typical for biochemical and physiological traits. To give an example, in a group of five clonemates of pig, the CVs for serum protein, blood urinary nitrogen and cortisol were 3.73%, 14.04%, and 28.98%, respectively. This semi-independency may depend on different buffering as explained later or on whether the biochemical trait of interest is based on regulation of the flux through a pathway or regulation of concentrations. Flux regulation may be accompanied by larger variations in the levels of intermediates (Cornish-Bowden and Nanjundiah 2006).
5.2.3 Life history traits
Life history traits comprise reproduction, life span, and mortality. They are particularly important because they markedly influence demography and fitness. An example of SDV of reproductive traits is presented in table 2 for 8 batchmates of marbled crayfish reared communally from hatching to death. The time between hatching and first spawning varied from 157 to 531 days, the number of breeding cycles from 1 to 5 and the number of offspring per female at day 430 from 0 to 219, corresponding to CVs of 52.46%, 49.52%, and 90.68%. The allocation of metabolic resources towards reproduction and growth varied broadly in this group as well (figure 5E). Other examples of SDV of reproductive traits come from batchmates of parthenogenetic water flea, which had different numbers of embryos in their brood chamber at a given time (figure 5D). Under specific laboratory conditions, these crustaceans can produce either normal eggs or differently structured resting eggs, and this feature was explained to about 98% by SDV and only to 2% by environmental heterogeneity (Lajus and Alekseev 2004).
Life span is also quite variable among identically reared clonemates. For instance, in a group of 14 inbred mice this trait varied from 27 to 147 days, and in 16 inbred rats from 16 to 113 days (Williams and Pelton 1966). In the above-mentioned group of 8 marbled crayfish, reproducing specimens reached ages between 437 and 910 days, corresponding to a CV of 21.31% (table 2). It has to be noted that this value does not include life span data of batchmates that had died prior to maturity. Broader spectra of relative life spans ranging from 3 to 30 days and 3 to 55 days were found in two strains of Caenorhabditis elegans (figure 5F) in which all group members were considered.
5.2.4 Behavioural traits
SDV of behaviour is less well investigated than SDV of morphological, biochemical, and life history traits but there are some interesting examples published for clonal fish and mammals. For example, Archer et al. (2003b) quantified food preference, temperament and time budget in cloned pigs and found that intraclonal variation could reach dimensions comparable to variation in naturally bred control groups (figure 6A). Tordoff et al. (2007a, b) investigated voluntary uptake of water, sodium and calcium in 40 strains of inbred mice and found considerable variation between and within strains. Iguchi et al. (2001) analysed SDV of movement, feeding, alerting and threat behaviour in clonal salmon Oncorhynchus masou macrostomus and revealed CVs between 96.23% and 215.74% (table 2), indicating pronounced behavioural individuality. Turko et al. (2011) examined voluntary emersion in isogenic strains of self-fertilizing mangrove rivulus Kryptolebias marmoratus and found that clonemates spent very different proportions of their time (0-78%) out of water. Interestingly, these behavioural differences were coupled with differences in gill morphology.
SDV of behavioural traits in animals. (A) Variation of time budget in two litters of cloned female pigs from the same fetal cell line in comparison to two naturally bred litters (from Archer et al. 2003b). (B) Variation of agonistic behaviour and concomitant growth differences in batchmates of marbled crayfish kept for 34 days under conditions of social stress. The experiment was started with five size-matched batchmates of indifferent agonistic behaviour and ended with one dominant (d), two subdominants (sd) and two subordinates (s) of remarkably different size, although food was available in excess and not monopolized (from Vogt et al. 2008).
There are also some interesting examples of SDV of behaviour in invertebrates. For instance, Schuett et al. (2011) recorded different escape responses to predator attack among clonemates of the parthenogenetic pea aphid: dropping off the plant, non-dropping, and inconsistent behaviour. In marbled crayfish, variation of behaviour among batchmates became obvious as soon as the juveniles started to move and feed (Vogt et al. 2008). Juveniles are principally able to move from stage 2 on, but under natural conditions, this life stage is firmly hooked onto the maternal pleopods with close contact to each other. When such juveniles were taken from the pleopods and evenly distributed on a net, they first roamed around and then hooked into the net to stay in this position for several days. Of 38 batchmates, 6 preferred to hook in individually, while the other 32 adhered in smaller groups with the individuals keeping antennal contact to each other.
A remarkable divergence of behaviour from the same initial status occurred when stage-6 batchmates of marbled crayfish were placed in groups of five into culture vessels without shelter (Vogt et al. 2008). In the following 34 days, social hierarchies were gradually established. At the end of the experiments, each group consisted of 1 dominant, 1–2 subdominants and 2–3 subordinates. During establishment of the hierarchies, the dominants developed increasingly offensive behaviours, whilst their counterparts developed increasingly defensive and avoiding behaviours. Interestingly, growth of the dominants speeded up compared to the subdominants and subordinates (figure 6B) although all specimens had unlimited access to the food and fed regularly as revealed by the externally visible filling of the intestine. These differences in behaviour and growth probably developed from very small behavioural differences via self-reinforcing circuitries involving metabolism and neuroendocrine feedback. Such pronounced differences were not established when shelters were available.
5.3 Intra-individual SDV
This section deals with stochastic developmental differences between mirror symmetrical structures of bilaterian animals and serially repeated parts of homonomously segmented animals.
5.3.1 Phenotypic variation between body sides in bilaterally symmetrical animals
The right and left body sides of bilaterally symmetrical animals include the same genes and are normally exposed to the same environmental conditions. Therefore, bilaterally paired structures should be identical but this is only rarely the case. The random asymmetry around a zero mean value is called fluctuating asymmetry (FA) (Van Valen 1962; Palmer and Strobeck 1986; Schilthuizen and Gravendeel 2012). FA can be caused by genetic disturbances, environmental stress, developmental stochasticity, or a mixture of them (Parsons 1992; Graham et al. 2010). Babbitt (2008) partitioned FA of wing parameters in a monoclonal population of cotton aphid Aphis gossipyii raised along a temperature gradient from 12.5°C to 25°C and found that developmental stochasticity was responsible for about 50% of the overall response of FA. In this section I have only considered laboratory studies with isogenic populations in which genetic and environmental stresses should be the same for all group members.
FA data come mainly from external morphological traits such as colour pattern, metric body parameters, or the quantity of setae (figure 7A–C). Internal characters like skeletal elements or blood vessel patterns (figure 7D) were less frequently used. Arthropods are particularly suitable for research on FA because they have a rigid exoskeleton studded with numerous appendages and sensory setae allowing exact measurement and counting. Moreover, in longer-lived indeterminately growing species like marbled crayfish, the shed exuviae provide an excellent archive for individual longitudinal studies.
Fluctuating asymmetry (FA) in laboratory reared animals. (A) FA in dorsal thoracic bristles of fruit fly. Left panel shows location of bristles in animal (arrow). Right scanning electron micrograph shows bristles in detail and gives ranges of FA for bristles on notum (n) and scutellum (s) in 11 inbred strains (left photo: Pascal Radtke; right photo: Eric C Lai; data from Indrasamy et al. 2000). (B) Asymmetry of veins (arrows) in right and left wings of cotton aphid (from Babbitt 2008). (C) Differences in marmoration pattern between left and right posteriolateral cephalothorax areas of marbled crayfish. Arrows point towards head. (D) Differences in retinal blood vessel pattern between right and left eye of young man (photo: Mikael Häggström). (E) Variation of FA of meristic traits among batches and juvenile stages of marbled crayfish. The graph includes data on the numbers of aesthetascs (Ae) and corrugated setae (CS) of four batches derived from mothers A, B, B1 and B3. Dams A and B were from different laboratory lineages, and dams B1 and B3 were daughters of dam B. Each batch was analysed from juvenile stage 2, in which the sense organs appear, to juvenile stage 6 (stages indicated by numbers on bars). FA is expressed as percent deviation from perfect symmetry (Sym) to the right or left body side. Red horizontal bars indicate mean of all stages of a batch. Note differences of FA between batches, temporal dynamics within batches, and semi-independent behaviour of traits.
A particularly high variation between body sides was recorded for colour patterning of the integument in parthenogenetic marbled crayfish (figure 7C), inbred guinea pigs (Chase 1939), and cloned Holstein cattle (Seidel et al. 2003). The same holds for sexually reproducing species with spotted or striped colour coats (Murray 2003). High right-to-left differences are also common in fingerprints, irises, and retinal vessels of humans (figure 7D) and in noseprints of cattle (figure 3B) (Markow and Gottesman 1989; Daugman and Downing 2001; Yang et al. 2012). These differences are already well established in newborn babies or calves, which have experienced the rather uniform environment of the uterus only.
FAs of metric and meristic morphological traits are usually less prominent. For example, in inbred mice strains, FA of femur length, humerus length, tibia length, and mandible centroid size varied from about 2 to 5% (Leamy et al. 2001). Higher FA values were recorded for the number of bristles on the head and thorax of wild type and 10 mutant strains of Drosophila (figure 7A). In these lineages, mean FAs of the ocellar, notum, and scutellar bristles varied from 11 to 24%, 0 to 14%, and 0 to 20%, respectively (Indrasamy et al. 2000). Interestingly, FAs of the three traits also showed considerable within-strain variation. For example, in mutant strain Ax9B2 the ocellar, notum and scutellar bristles had FAs of 20%, 11%, and 4%.
In marbled crayfish, FAs of the numbers of aesthetascs and corrugated setae varied considerably between batches from different mothers and between consecutive juvenile stages of the same batch. They mostly fluctuated between 3% right asymmetric and 3% left asymmetric, having extremes of about 6% (figure 7E). FAs of the two traits were usually not correlated, as shown by the analysis of the offspring of 4 mothers across 5 juvenile stages. Quite often, FAs of both traits even had an opposing symmetry in a given batch. Moreover, FAs shifted with ongoing development from right asymmetric to left asymmetric and vice versa, suggesting that asymmetries may be corrected when they exceed a certain range.
This idea is supported by longitudinal analysis of the exuviae of individuals reared under constant environmental conditions (Vogt et al. 2008). For example, in a specimen raised from juvenile stages 6 to 14, FA of the aesthetascs shifted from left asymmetric to symmetric to right asymmetric and back to left asymmetric. A similar but chronologically postponed pattern was observed for the corrugated setae of the 2nd pereopods, whereas the corrugated setae of the 1st pereopods remained right asymmetric all the time. These results corroborate that different traits behave as semi-independent modules, as earlier shown for inter-individual SDV.
Good examples of FA of internal structures aside of vertebrate skeletal elements and teeth are the retinal blood vessels of humans (figure 7D) and the unpaired descending artery of marbled crayfish. This artery originates from the unpaired heart, which is located dorsally in the median plane, runs around the hindgut on either side and terminates ventrally in the subneural artery that is located in the median plane as well. Serial sectioning of 133 identically reared juveniles revealed that the descending artery was right asymmetric in 45.1% of specimens, left asymmetric in 50.4%, and bilaterally symmetric in 4.5% (Vogt et al. 2009). In the symmetric variant, two branches arise from the heart instead of one, pass the hindgut on both sides, and fuse underneath to run as unpaired vessel to the subneural artery.
Comparison of intra-individual FA and inter-individual SDV of a given trait in the same batch of marbled crayfish revealed that FA is mostly smaller and not narrowly correlated with SDV. For instance, in 11 communally raised stage-5 batchmates the CVs of the number of aesthetascs on the 1st antennae and the number of corrugated setae on pereopods 1, 2 and 3 were 7.44%, 6.58%, 6.79%, and 5.61%, while the corresponding FA-values were 0.97%, 1.61%, 2.88%, and 2.55%. Görür (2009) made similar observations when investigating FA and inter-individual SDV of morphological traits in clonal cabbage aphid Brevicoryne brassicae.
5.3.2 Phenotypic variation between homonomous metameres in segmented animals
Information on developmental stochasticity was only rarely obtained from serially repeated body parts of homonomously segmented animals. An example is provided by Astauroff (1930), who compared the number of parapodial setae among more than 140 segments of the polychaete Nephthys caeca and the number of tarsal setae among 46 segments of the centipede Geophilus ferrugineus. After having normalized data across body length, there was still considerable variation in seta number per segment that fluctuated independently from the neighbouring segments. The author concluded from these data that each setagenic process has a certain degree of variability that is related neither to the genotype nor to the external environment.
5.4 Suitability of colonial and polyphenic invertebrates for research on SDV
Several sessile invertebrate groups such as the Porifera, Cnidaria, Bryozoa, and Tunicata use asexual budding to form colonies of genetically identical individuals. If such colonies are reared under controlled laboratory conditions that minimize environmental influences, then they can be used for research on SDV as well. Particularly attractive are colonies that have the individuals symmetrically arranged and that produce solid skeletal structures like many hydrozoans and bryozoans. As an example, Hageman et al. (1999) investigated six morphological characters in the bryozoan Electra pilosa (figure 8A) and found that the non-genetic and non-environmental residual variance can account for more than 50% of phenotypic variation, being the greatest for zooecium width. The authors suspected that this residual variation is attributable to developmental stochasticity. Likewise, Ponczek and Blackstone (2001) measured differences in morphological traits among isogenic polyps of laboratory-reared hydrozoans Hydractinia symbiolongicarpus and Hydractinia carnea.
SDV in colonial and polyphenic invertebrates. (A) Colony of bryozoan Electra pilosa showing different morphology of asexually produced zooecia (arrows) (photo: David Fenwick). (B) Polymorula of polyembryonic wasp Copidosoma bakeri including hundreds of genetically identical precocious (arrows) and reproductive larvae (arrowheads) of different morphology. There is also phenotypic variation within each of the two larval types (from Smith et al. 2010).
Polyphenism, the production of two or more discrete phenotypes in a population, was not yet exploited for research on SDV. The best investigated models of polyphenism, the aphids and social insects (Simpson et al. 2011; Srinivasan and Brisson 2012), are not suitable because the various morphotypes are determined by differences in ploidy and feeding. Better suitable are polyembryonic wasps of the genus Copidosoma, which generate different morphs in the same genetic and environmental context. These endoparasitic insects produce up to 3000 genetically identical embryos from a single zygote comprising two morphologically distinct larval castes (Smith et al. 2010). One caste remains larval all the time and acts as soldiers, whereas the other caste develops into reproducing wasps. Caste differentiation is determined by intrinsic factors of the waspʼs egg and early cellular asymmetry (Zhurov et al. 2004). Stochasticity in these processes may contribute to polyphenism and to phenotypic variation within castes (figure 8B).
6 Generation, mediation and limitation of SDV
In the previous sections I have shown that SDV occurs in all types of organisms and that its dimensions vary considerably between traits and species. This chapter deals with the sources of SDV, its dependency from the genetic and environmental contexts, its mediation by molecular epigenetic mechanisms, and its limitation by buffering mechanisms.
6.1 Generation of SDV at different levels of biological organization
SDV is produced by stochastic processes in cells, tissues, organs, and organisms. Stochasticity in the latter three may have its origin in intracellular stochasticity or may arise independently (Kilfoil et al. 2009). Here, I will present examples from bacteria and yeast for intracellular SDV and from animals for higher-level SDV. In plants, the SDV generating processes are just beginning to be identified (Meyer and Roeder 2014).
Many biochemical processes in the cell are inherently stochastic, particularly if small numbers of molecules are involved. Randomness in such reaction systems is inversely proportional to the square root of the number of participating particles (Meng et al. 2004). One of the highly stochastic cellular processes is gene expression, in which binding of transcription factors and polymerase is the result of random encounters (Elowitz et al. 2002; Kærn et al. 2005; Raser and O’Shea 2005; Casadesús and Low 2013). Moreover, transcription and translation occur in short bursts at random time intervals rather than in a continuous manner. A protein arising from stochastic gene expression can then lead to a cascade of stochastic downstream events. These sources of intracellular SDV occur in the protocyte, the cell type of archaeans and bacteria, and in the eucyte, the cell type of protists, fungi, plants, and animals. Further examples of SDV generating processes in the Prokaryota are bistability of metabolic pathways, antigen variation, chemotaxis, ageing, and persistence against antibiotics (Davidson and Surette 2008; Balaban 2011).
The eucyte has further possibilities to generate intracellular SDV due to the possession of histone-associated chromatin, microRNAs, membrane-bound cell organelles, and the cytoskeleton. The histones and microRNAs enable variation of gene expression beyond the mechanisms described for prokaryotes (Bartel and Chen 2004; Brown et al. 2013). The cell organelles contribute to SDV by variation in number and spatial distribution, the latter being mediated by the cytoskeleton (Johnston et al. 2012). Another important source of SDV comes from the stochastic partitioning of the cell components between the daughter cells, which is particularly important for unicellular eukaryotes (Huh and Paulsson 2011). Further information on intracellular SDV and the underlying mechanisms in eukaryotes is found in Veitia (2005) and Raj and van Oudenaarden (2008).
In multicellular protists, fungi, plants and animals, additional SDV generators evolved with respect to cell differentiation, patterning, organogenesis, growth, reproduction, behaviour, ageing, regeneration, and susceptibility to diseases. There are striking differences among the multicellular clades because of differences in body architecture, development, physiology, motility, and behaviour. For example, vascular plants are sessile and mechanically stable, have distinct stem cell systems with highly ordered proliferation patterns, are composed of dynamical patterning modules different from those of animals, and do not set aside germlines early in development as many animals do (Extavour and Akam 2003; Sablowski 2004; Barthélémy and Caraglio 2007; Hernández-Hernández et al. 2012). Animals, in contrast, are mostly motile and show sophisticated behaviours, which seem to be a particularly fertile source of SDV.
The involvement of SDV in differentiation and cell fate determination has been convincingly demonstrated for the slime mould Dictyostelium discoideum, one of the simplest models for studying cell differentiation. This amoebozoan forms temporary fruiting bodies composed of spore, stalk, and basal disc cells from a multicellular slug that originates from the aggregation of free-living amoebae. The CV of the fraction of cells that form the spores is mostly within 4.86%, whereas the contribution to stalk and basal disc cells is more variable, having CVs up to 24.5% and 100%, respectively (Nanjundiah and Bhogle 1995). Ràfols et al. (2014) established that the proportions of cells are strictly controlled above and below upper and lower thresholds but not in the range in between.
There are also several examples on the participation of SDV in cell fate decision in higher organisms (Balázsi et al. 2011). This process is usually interpreted as being deterministic; that is, cells acquire their fate by virtue of their lineage or their proximity to an inductive signal from another cell. In some cases, however, and in organisms ranging from bacteria to humans, cells choose one or another pathway of differentiation stochastically without apparent regard to neighbourhood or history (Losick and Desplan 2008). For example, animals use stochastic cell fate choices to govern priming of multipotent haematopoietic progenitor cells and to increase the repertoire of sensory and motor neuron subtypes (Chang et al. 2008; Johnston and Desplan 2010).
A prominent SDV generating mechanism in patterning is the stochastic expression of regulatory genes. Boettiger and Levine (2009) investigated the expression profiles of 14 developmental control genes in Drosophila embryos and found synchronous and stochastic patterns of gene activation. Synchronous genes displayed essentially uniform expression of nascent transcripts in all cells of an embryonic tissue, whereas stochastic genes displayed erratic patterns of de novo activation. Dietrich and Hiiragi (2007) found similar expression patterns during early embryonic development of mouse. Particularly, expression of Nanog, a transcription factor in embryonic stem cells that maintains pluripotency, exhibited a phase of random variability.
A particularly well investigated example of SDV during patterning is morphogenetic signalling. Medium- to long-distance morphogenetic signals are usually distributed as a gradient that is translated into more or less sharp boundaries (Nijhout et al. 2003; Veitia 2005). The nonlinear nature of such Turing reaction-diffusion and other patterning mechanisms (Turing 1952; Gierer and Meinhardt 1972; Murray 2003) is probably responsible for many differences among identically raised clonemates. Particularly impressive examples are the broad variation of spotted colour patterns in inbred guinea pigs, cloned Holstein cattle, and parthenogenetic marbled crayfish. Such nonlinear patterning mechanisms also contribute to the highly diverse colour patterns in sexually reproducing species like leopards, zebras, gastropods, or butterflies (Murray 2003; Nijhout et al. 2003; Meinhardt 2009). In embryos of vertebrates, the melanocytes originate in the neural crest and migrate from there to their final destination in the skin. Since this cell migration includes a considerable probabilistic component that increases with distance, variation of colour patterning is higher in the legs than in the proximity of the backbone (Seidel et al. 2003).
The malacostracan crustaceans, which include the marbled crayfish, are particularly good subjects to study SDV during organogenesis. They display a stereotyped cell division pattern, offering the possibility to study the relationship of cell lineage, gene expression, and organ development down to the single-cell level (Scholtz 1992; Dohle et al. 2004; Alwes and Scholtz 2006). By using the Distal-less protein as a marker, Dohle et al. (2004) revealed that in the amphipod Orchestia cavimana, corresponding limbs are formed by different genealogical cell lines, indicating nonlinearity of morphogenetic signalling. Stochasticity in organogenesis can also come from differences in the timing of cell cycles within expanding cell populations. As an example, in imaginal wing discs of Drosophila melanogaster cell synchrony is maintained over two to four doublings only (Milán et al. 1996). Transdetermination, the change in determined state in Drosophila imaginal discs, also includes SDV components due to the interaction of cell-autonomously and non-cell-autonomously induced signals (Maves and Schubiger 1998). Further examples of SDV in patterning and organogenesis are found in Kilfoil et al. (2009).
A largely neglected generator of developmental stochasticity is mechanical forces, which is particularly relevant for bigger animals and plants (Newman and Bhat 2007; Uyttewaal et al. 2012). Newman and Bhat (2007) discussed the interplay between molecular-genetic and dynamic physical processes in pattern formation of the vertebrate limb and assumed that the nonlinear physical processes contribute to SDV of limb traits in a manner similar to the nonlinear chemical processes. The broad variation of fingerprints in humans depends to a large degree on such nonlinear physical processes as well. The epidermal ridge pattern is established between the 10th and 16th week of pregnancy as the result of buckling instability acting on the epidermis. This buckling process is controlled, among others, by shrinkage of the volar pads (Kücken 2007).
The production of SDV in adult animals is less well investigated than in earlier life stages although it may contribute much to ageing and diseases (Finch and Kirkwood 2000; Aranda-Anzaldo and Dent 2003; Martin 2014). Here, I would like to discuss three SDV producing mechanisms in more detail: stem cell proliferation, behaviour, and the allocation of metabolic resources. In the latter two, small differences between clonemates are thought to be generated by SDV and then be amplified by self-reinforcing circuitries.
In adult tissues, there is a balance between loss of cells and replacement by stem or progenitor cells. Stem cell proliferation includes stochastic components with respect to spatiotemporal activity, cell fate decision, and self-renewal. The latter relies upon cell-autonomous regulation (Simons and Clevers 2011). SDV also seems to be involved in the stimulation of cancer stem cell development via stochastic fluctuations in intercellular signalling (Dos Santos and da Silva 2013). Stochasticity generation by adult stem cells is more relevant for indeterminate than determinate growers because of the higher abundance and activity of stem cells in the former (Vogt 2012).
SDV of behaviour seems to be a particularly strong elicitor of phenotypic diversity. For example, the individual decision to eat a bit more or a bit less might first lead to slight differences in growth of clonemates, then to more pronounced differences in food uptake followed by further divergence in growth. Such self-reinforcing circuitries include behaviour, metabolism, and neuroendocrine feedback. They probably also drove the concomitant divergence of agonistic behaviour and growth observed in marbled crayfish batchmates raised without shelters.
Animals allocate their metabolic resources towards maintenance, growth, and reproduction. In adult marbled crayfish, growth periods and reproduction periods are alternating, and lost appendages are readily regenerated. Small initial differences in the allocation pattern among batchmates can lead to striking differences in future life history. For example, if a crayfish by chance loses one of his appendages, it may invest a higher proportion of energy and metabolites in regeneration, which may slow down growth and/or reproduction compared to its batchmates. On the other hand, a crayfish that by chance starts to reproduce earlier may grow to a smaller final size or live shorter than its batchmates.
So far, I have shown that SDV can be produced from the zygote to old age, but are there particularly sensitive life periods for SDV generation? The answer depends very much on the trait and the life history of the species considered. As a rule of thumb, embryonic development seems to be of prime importance in determinately growing mammals and insects, whereas the adult life period appears equally important in indeterminately growing crustaceans, molluscs, and fish. For example, in mammals differences in fingerprint patterns and spotting of the integument arise during the mid and late period of embryonic development and persist more or less unchanged throughout life (Kücken 2007; Caballero et al. 2012). In marbled crayfish, differences in marmoration start to develop in late embryogenesis and increase throughout the approximately 25 juvenile and adult moultings (Vogt et al. 2008). Likewise, differences in body weight among littermates of inbred rat were shown to have their roots in stochastic events in the zygote and first cleavage stages (Gärtner 1990, 2012), whereas in marbled crayfish such differences are more attributable to SDV generation in the adult life. SDV-related differences in ageing and diseases may have their origins in both early development and the later life (Aranda-Anzaldo and Dent 2003; Kirkwood et al. 2005; Vogt et al. 2008; Vogt 2010; Martin 2014).
6.2 Dependency of SDV from the genetic and environmental contexts
Although SDV is a non-genetic and non-environmental source of phenotypic variation, its absolute values and ranges are dependent on the genetic and environmental contexts. The relationship between SDV and the genetic background is easiest demonstrated by comparison of a given trait between species from different higher taxa. For instance, the range of longevity in a group of marbled crayfish was 437–910 days, whereas it was 3–31 days in a colony of the nematode Caenorhabditis elegans. Another example is provided by Gärtner (1985), who measured different ranges of SDV in six identically reared but genetically different lineages of inbred rat. Further evidence comes from the comparison of the CVs of different traits in the same group of animals, which varied from less than 1% to more than 100% in an armadillo quadruplet (Storrs and Williams 1968). These differences are supposedly the result of the modular architecture of animals and the semi-independency of the modules (Wagner et al. 2007). Finally, yet importantly, it has been shown that genetic mutations can change the range of stochastic variation in gene expression (Miller-Jensen et al. 2013; Richard and Yvert 2014).
The dependency of SDV from the environmental context must not be confused with the environmental proportion of phenotypic variation, which is usually expressed as the norm of reaction (Schlichting and Pigliucci 1998). A good example of the dependency of SDV from the environmental context was published by Gärtner (1985). He measured considerable differences in SDV of kidney weight among groups of rats that were genetically identical and of the same gender and age but raised in different environments. These examples indicate that the genetic and environmental contexts provide a framework for SDV that is newly adjusted when the genome or the environment changes.
6.3 DNA methylation as an example for the molecular mediation of SDV
As outlined previously, there is already a bulk of information on the SDV generating processes but the underlying molecular mechanisms are only poorly understood. Among the mechanisms that mediate the production of different phenotypes from the same genotype is DNA methylation, which is effective in all organisms from bacteria to man (Rakyan et al. 2002; Jaenisch and Bird 2003; Feinberg and Irizarry 2010; Casadesús and Low 2013). Alteration of this chromatin modification, either stochastically or by the input of environmental signals, can change gene function and result in phenotypic variation (Jaenisch and Bird 2003; Weigel and Colot 2012).
The mediating role of DNA methylation is better investigated for environmentally induced phenotypic plasticity than for SDV and comes from laboratory and field studies. For example, Lyko et al. (2010) analysed DNA methylation at single-base-pair resolution in honeybee queens and workers and found variation of the methylation pattern in more than 500 genes. They concluded that the phenotypic differences between the two morphotypes that is caused by different feeding is implemented in concert with DNA methylation. Liebl et al. (2013) investigated genome-wide DNA methylation in expanding populations of house sparrow Passer domesticus, which was introduced to Kenya in the 1950s. They found high levels of variation in methylation across the genome and a negative correlation between epigenetic and genetic diversity, suggesting that DNA methylation mediates the observed phenotypic plasticity. Bossdorf et al. (2010) manipulated the DNA methylation pattern in Arabidopsis thaliana and revealed dramatic impacts on ecologically important traits and their variability.
To my best knowledge, genome-wide analysis of DNA methylation at single-base-pair resolution, the most sensitive method available, has not yet been applied to SDV. However, other approaches like the correlation of the methylation pattern of specific genes with phenotypic variation have already been utilized. For example, Field and Blackman (2003) demonstrated that the stochastic loss of resistance to an insecticide in clonemates of the peach-potato aphid Myzus persica was caused by changes in methylation of the esterase genes. Rakyan et al. (2002) found that the agouti viable yellow allele Avy, which underlies different coat colours in isogenic mice, could stably exist in several epigenetic states. These states are established during early embryogenesis by stochastic DNA methylation events and can be transgenerationally inherited. Kaminsky et al. (2009) analysed epigenetic metastability of ~6000 genomic regions in human monozygotic twins and concluded that stochastic epigenetic variation is probably more important than environmental variation to explain phenotypic differences among monozygotic twins.
6.4 Limitation of SDV by buffering mechanisms
SDV would probably produce a bewildering array of phenotypes from the same genotype if it were not counteracted by control mechanisms. Researchers working on SDV of unicellular organisms usually speak of ‘noise control’ (Kim and Sauro 2012), whereas researchers working on multicellular organisms speak of ‘developmental buffering’ (Debat and David 2001; Patterson and Klingenberg 2007). Control of SDV includes feedback loops, redundancy and modularity, clustering of genes with similar noise, and specific activities of control genes and control molecules (Klingenberg 2003, 2006; Kitano 2004; Levy and Siegal 2008; Wang et al. 2011).
In bacteria, negative and positive feedback loops and feedforward loops are apparently of prime importance for noise control (Jablonka and Raz 2009; Kim and Sauro 2012; López-Garrido et al. 2012). In pathogenic bacteria, such loops often lead to an infective and a non-infective phenotype (Smits et al. 2006). Similar feedback loops are also effective in eukaryotic cells. Nanjundiah and Bhogle (1995) and Ràfols et al. (2014) explained SDV in cell differentiation of the social amoeba Dictyostelium discoideum by cell-autonomous positive feedback and global negative feedback. Zordan et al. (2006) investigated stochastic switching between white and opaque phenotypes of the human fungal pathogen Candida albicans, which differ in morphology, gene expression, and infectivity, and found that stabilization of these phenotypes involves a self-sustaining positive feedback loop. Once activated, this loop persists for several generations. Avendaño et al. (2013) investigated the galactose uptake system of yeast and found that positive feedback loops stabilize different phenotypic states, whereas negative feedback loops allow tuning of the range and transition rates between phenotypic states.
Another mechanism for control of SDV in bacteria and yeast is grouping of genes with similar noise. Investigations in Escherichia coli and Saccharomyces cerevisiae revealed that proteins and their coding genes can vary considerably with respect to SDV. Essential proteins like those involved in protein synthesis have low noise and less-essential proteins like those responding to environmental changes have high noise under multiple conditions, suggesting that SDV of proteins has been selected during evolution (Fraser et al. 2004; Newman et al. 2006). In yeast, persistently open chromatin domains are apparently preferred sites for clustering of essential genes as they enable noise reduction by avoidance of transcriptional bursting associated with chromatin remodelling (Batada and Hurst 2007).
In multicellular organisms, developmental buffering is often subdivided into canalization, the buffering against genetic and environmental variation, and developmental stability, the buffering against stochastic developmental variation. The relationship between canalization and developmental stability was subject to debate in the past. Some authors have suggested that there is only one mechanism accounting for developmental homeostasis, while the majority of authors have proposed two independent processes that are somehow linked to each other (Debat et al. 2000; Milton et al. 2003; Breuker et al. 2006).
In the last decade, some genes with buffering function have been identified in animals and plants. For example, cyclin G, a regulator of transcription and cell cycle, plays a major role in developmental stability of Drosophila melanogaster, suggesting that phenotypic robustness can be strongly influenced by individual genes (Debat and Peronnet 2013). Another example is the gene ERECTA, which contributes to canalization of rosette leaf number in Arabidopsis thaliana under long-day photoperiod (Hall et al. 2007).
SDV-buffering molecules include signal transduction proteins, heat shock proteins, and microRNAs. An example of the former is Wnt, which filters SDV during development and pattern formation (Arias and Hayward 2005). Heat shock protein Hsp90 was the first protein identified to be involved in developmental buffering (Queitsch et al. 2002; Milton et al. 2006; Sangster et al. 2008). It is one of the most abundant proteins expressed in cells, is well known as a chaperone of many regulatory proteins, and can canalize phenotypes by suppressing the underlying genetic and epigenetic variation in a trait specific manner (Milton et al. 2003; Patterson and Klingenberg 2007; Salathia and Queitsch 2007). Meanwhile, other heat shock proteins have been shown to be involved in developmental stability as well (Takahashi et al. 2010). Knockdown of Hsp67Bc and Hsp22 significantly increased fluctuating asymmetry of bristle numbers in Drosophila melanogaster, and knockdown of Hsp67Ba increased both FA and inter-individual variation of wing shape.
MicroRNAs are abundant gene-regulatory molecules constituting almost 1% of the genes in animal genomes. They repress the expression of protein-coding mRNAs through binding of a minimal-recognition sequence and are key players in noise control (Herranz and Cohen 2010; Shomron 2010). They are also involved in differential expression of homologue alleles within cells and incomplete penetrance, contributing to the lack of genotype-to-phenotype correlation (Ahluwalia et al. 2009). Hornstein and Shomron (2006) assumed that the interaction of microRNAs with the network of protein-coding genes evolved to buffer stochastic perturbations, thereby conferring robustness to developmental programmes. Li et al. (2009) found that miR-7, which is widespread in animals, functions in several feedback and feedforward loops, and is essential to buffer these regulatory systems against environmental perturbations. Osella et al. (2011) modelled the buffering role of microRNA-mediated feedforward loops, in which a master transcription factor regulates a microRNA and, together with it, a set of target genes.
If DNA methylation is an important mediator of SDV as outlined above, then proteins involved in the establishment and erasure of the methylation patterns and their interpretation should play a role in the control of SDV as well. These are the DNA methyltransferases that mediate cytosine methylation (Goll and Bestor 2005), the recently discovered ten-eleven translocation enzymes that mediate DNA demethylation (Kohli and Zhang 2013), and the methyl-CpG binding domain proteins that play an important role in interpreting the genetic information encoded by methylated DNA (Zou et al. 2012).
7 Implications of SDV for genotype-to-phenotype mapping, individuality, and personality development
There is no doubt that an individual DNA-sequence has the capacity to map to different phenotypes. This is impressively shown by the origination of more than 400 structurally and functionally different cell types from the single genome of the zygote in humans (Vickaryous and Hall 2006). It is also well known that a genotype sensu strictu can translate into multiple phenotypes under different environmental conditions (Scheiner 1993; Nanjundiah 2003; Pigliucci 2010). For instance, if genetically identical stolons of the same plant are grown at different altitudes or in different soils they usually develop different morphologies, and when diploid honeybee larvae are fed on either royal jelly or pollen they develop into morphologically and functionally distinct queens and workers, respectively (Schlichting and Pigliucci 1998; Simpson et al. 2011).
Despite of these examples on the plasticity of the genotype, it is widely believed that in a given environment a genotype produces one phenotype only. However, laboratory experiments with clonal organisms in standardized environments have clearly shown that even under these conditions, multiple phenotypes are produced from a single genotype, thanks to SDV. This finding corroborates that the very nature of genotype-to-phenotype mapping is not one-to-one but one-to-many. Newman and Müller (2000) argued that in animals the relationship of genotype and phenotype was looser in their early evolution than today and became constrained only by the evolution of canalization, in which an ‘over-determining’ genetic circuitry ensures that changes of intrinsic or extrinsic variables have less impact on the morphological outcome.
The concept of an individual is vague in biological philosophy (Buss 1987; Wilson 2007; Kupiec 2009). Paradigmatic biological individuals are members of a population that are distinct from others. Wilson (1999) distinguished six different concepts of individuality: an individual can be a particular, a historical entity, a functional individual, a genetic individual, a developmental individual, or a unit of selection. Some authors extended the term ‘individuality’ even to other distinct entities of life that may act as units of natural selection such as genes, cell organelles, cells, developmental patterns, functional modules, life cycles, and even colonies, populations and species as discussed in Lewontin (1970), Hull (1980) and Gould and Lloyd (1999). Interestingly, SDV can contribute to variation of all of these aspects of individuality up to the level of the organism, with the exception of genetic individuality, which is the realm of genetic variation.
The processes that generate differences among individuals have been discussed for more than a century in the framework of the nature–nurture debate (Galton 1876; Overton 1973; Fox Keller 2010). This debate was spurred by biologists, social scientists, and politicians and was the basis for the paradigm clash between genetic determinism and Lysenkoism, among others. Traditionally, members of sexually reproducing populations are considered as distinct individuals because of genetic differences, whereas members of asexually reproducing populations are seen as identical copies. However, thanks to SDV, members of clonal populations must be regarded as distinct individuals as well. They are not genetic individuals but morphological and functional individuals. This even holds for bacteria as revealed by single cell analyses (Avery 2006; Ackermann 2013; Nikel et al. 2014), suggesting that individuality is a general attribute of all living beings.
The observation of SDV-caused individuality in clones of unicellular organisms served as a stimulus to interpret the tissues and organs of multicellular organisms as structural and functional mosaics instead of uniform entities (Woods 2014; Yvert 2014). This insight has considerable consequences for our understanding of diseases and may constrain highly publicized personalization of medicine by genomic approaches (Chan and Ginsburg 2011).
The term personality refers to behaviour-based peculiarities that differ across individuals and, once established, remain relatively consistent over time (Stamps and Groothuis 2010). It is probably the most complex and most variable trait, at least in higher mammals. It has been seen for long as an exclusive feature of humans because it was thought to require a complexity and subtlety that is unique to man (Bell 2007), but in recent years the concept of personality has been extended to animals as well (Stamps and Groothuis 2010; Trillmich and Hudson 2011). Understanding of the relative contribution of genetic and non-genetic factors in shaping personality traits is of fundamental interest to biologists, psychologists, and social scientists (McCrae et al. 2001).
Gosling (2008) emphasized that due to easier genetic manipulation and standardization of the living conditions animal studies may provide unique opportunities to disentangle the genetic, environmental, and stochastic developmental bases of personality and to study personality changes with age and disease. The stochastic developmental proportion of personality is best investigated with clonal animals. Experiments with inbred mice, cloned pigs, and parthenogenetic pea aphids and marbled crayfish have shown that SDV can cause personality differences among isogenic batchmates, indeed (Archer et al. 2003b; Vogt et al. 2008; Lewejohann et al. 2011; Schuett et al. 2011). Particularly impressive is the marbled crayfish example, in which batchmates diverged into dominants, subdominants, and subordinates despite shared environmental conditions and initially identical size and behaviour (Vogt et al. 2008).
In monozygotic human twins, the non-shared environment is often seen as the main cause of differences in personality, cognitive abilities, and psychopathology (Plomin and Daniels 1987; Plomin 2011). However, Turkheimer and Waldron (2000) did not find such a correlation in their data and suspected developmental instability as a major causative factor. Molenaar et al. (1993) also argued that there must be a third source of variation in addition to genetic and environmental factors coming from nonlinear epigenetic processes and creating variability at all phenotypical-somatic and behavioural levels. The authors also introduced a model to quantify these influences.
Behaviour and the brain are the main foundations underlying personality in animals and humans (LeDoux 2002; Canli 2006). Behaviour is central to personality by definition and the brain is the final pathway through which genetics, environmental influences, and stochastic developmental factors operate together (Van Praag et al. 2000; Davidson 2001). Interestingly, the degree of SDV is particularly high in behaviour and wiring and functioning of the brain (Clarke 2012). Therefore, personality development could hardly be forecasted even if the DNA sequence of an individual and its future living conditions were known.
The significant contribution of SDV to personality also has implications for the ethical debate on reproductive cloning of humans (Elliott 1998; Brock 2002). Brock (2002) raised concerns that cloning of humans might undermine our sense of self, the value of human beings, and the freedom to construct the own life because of the presence of an earlier twin. These arguments against human cloning can be refuted because, due to SDV, clones will never have the same personality as their donors or earlier clonemates. Nevertheless, cloning of humans and animal companions still makes little sense because what are desired are copies of the personality and not just of the form of the beloved person or pet. Because of the high proportion of SDV in personality development, human or animal personalities cannot be eternalized and vanish with the death of the individuals.
8 Implications of SDV for applied biology and medicine
SDV affects several fields of applied biology and medicine including animal and plant production, breeding of beneficial microbial strains, pharmaceutical and toxicological testing, infectivity and resistance of pathogens, and progression and treatment of non-infectious diseases.
8.1 Impact of SDV on animal and plant production, biotechnology, and biological testing
In applied animal biology, SDV is usually perceived as a nuisance biasing attempts to produce standardized animals for husbandry, aquaculture, and animal testing. The first method developed to produce homogeneous animals was inbreeding but even in highly inbred strains, the degree of phenotypic variation remained remarkably high due to SDV (Gärtner 1990). Later, cloning methods like twinning and somatic cell nuclear transfer were developed as the ultimate tool to create copies of a desired phenotype and to average out individual variation. Contrary to expectation, such clonemates were phenotypically variable as well, which was often attributed to insufficiencies of the cloning technique (Jaenisch and Bird 2003; Smith and Murphy 2004). However, a considerable proportion of this phenotypic diversity is obviously caused by SDV and would persist even after perfecting the cloning technique. It depends very much on the target trait and expectation of the customer whether cloning of animals is useful or not. For example, cloning is a rather successful approach to increase milk yield in cattle because SDV of this trait is relatively small (Seidel 2002) but it is rather unsuitable to copy pets with attractively spotted integuments (Shin et al. 2002) because SDV of this trait is very high.
In plant production, SDV is perceived as a nuisance as well if valuable strains are to be propagated in true-to-type form. The clonal multiplication of plants from cultured cells and tissues was expected to solve this problem, but contrary to expectation, phenotypic variation persisted or was even increased, which is called somaclonal variation (Karp 1994; Miguel and Marum 2011). Meanwhile, it is well known that the degree of SDV is often higher in animal and plant cell cultures than in the respective source organs. Rubin (1990) explained this phenomenon with the absence of buffering effects exerted by higher levels of organization. The disturbing effects of SDV in cell culture could be reduced if the sources of SDV and their buffering mechanisms were better known (Pilbrough et al. 2009). On the other hand, SDV may be exploited to improve crop or to develop new flower varieties (Karp 1994). The same holds for bioreactors, in which SDV usually generates microbial heterogeneity (Patnaik 2006; Delvigne and Goffin 2014).
In biological tests for drug development and chemical risk assessment SDV has been feared for long as a disturbing factor (Gärtner 1990; Adelman 2005). Such tests are routinely performed with genetically identical organisms like inbred mice and rats or parthenogenetic water fleas. They are the basis for the calculation of threshold values like ‘threshold of toxicological concern’, ‘no-observed-effect level’, or ‘acceptable daily intake’ (Kroes et al. 2000). Such values are often mandatory by law but due to the SDV-related fuzziness inherent to the test organism, they should be regarded as guidance values rather than the absolute truth. Adelman (2005) stressed that the susceptibility to toxic exposure is highly variable from person to person not only because of genetic differences and different living conditions but also because of SDV, and that environmental law should consider this variation.
8.2 Impact of SDV on virulence and resistance of pathogens, and medication of diseases
The prime disease agents of humans and animals are asexually reproducing viruses, bacteria, protists, and fungi with clonal population structures (Tibayrenc and Ayala 2012). For example, 72% of the 179 known eukaryotic human pathogens and parasites are clonal, affecting more than 1339 million people worldwide (De Meeûs et al. 2009). The same holds for agricultural pests like mitosporic fungi and parthenogenetic insects (Taylor et al. 1999; Hoffmann et al. 2008), which cause high yield losses and considerable costs of pest control. Worldwide, approximately 3 million tons of pesticides with a purchase price of US$ 40 billion are annually applied (Pimentel 2005).
In order to optimize virulence and to evade immune surveillance, clonal disease agents and pests produce phenotypic diversity at their cell surface either by genetic or epigenetic mechanisms (Avery 2006; Verstrepen and Fink 2009; Casadesús and Low 2013). The role of genetic variation in pathogenesis is much better investigated than the role of stochastic or environmentally induced epigenetic variation (Calo et al. 2013). Whether the genetic or epigenetic sources of phenotypic variation are predominant seems to depend very much on the pathogen. For example, the gastric-ulcers-causing Heliobacter pylori and the gonorrhoea-causing Neisseria gonorrhoeae show rapid genetic changes, whereas the plague-causing bacterium Yersinia pestis and the typhus-causing Salmonella enterica enterica ser. Typhimurium are essentially a single clone (Maynard Smith et al. 1993; Spratt 2004). Consequently, the latter depend much more on SDV than the former to enhance infectivity. Freed et al. (2008) revealed that the genomic fragments that conferred the highest levels of SDV in Salmonella were promoters controlling the synthesis of flagella, which are essential for host–pathogen interactions and virulence.
Further medical topics, in which SDV is involved, are the resistance of disease agents against antibiotics and the latency of viruses. In bacteria, there are two types of resistance: the first and better-known type relies on stable genetic changes caused by recombination and mutation, and the second type relies on stochastic epigenetic alterations (Dhar and McKinney 2007). The escapers of the latter category are called ‘persisters’ and their refractoriness to antibiotics is often metastable and transient. The self-induced transient resistance to antibiotics in Dictyostelium discoideum observed by Kasbekar et al. (1985) seems to have an SDV component as well. Patra and Klumpp (2013) assumed that phenotype switching in isogenic bacterial pathogens is mostly a stochastic epigenetic process independent of the environment and provided a theoretical framework to explain this phenomenon. Adam et al. (2008) investigated the evolution of SDV-based resistance against antibiotics in Escherichia coli and found that variant gene expression patterns in an isogenic population were epigenetically mediated, inherited, and preserved for multiple generations. In pathogenic viruses, fate decision between lysogeny and latency is controlled by stochastic gene expression in key auto-regulatory proteins as shown for HIV-1 (Singh and Weinberger 2009).
SDV is also involved in the onset and progression of non-infectious diseases including psychiatric disorders and cancer (Woolff 1997; Bosl and Li 2010; Feinberg and Irizarry 2010). For instance, intra-tumour heterogeneity, the ability to seed metastases, and the survival of tumour cells to therapy are often associated with stochastic variation of DNA methylation (Hansen et al. 2011; Marusyk et al. 2012). SDV is also at play during ageing and is seen important for the understanding of processes that lead to age-related frailty, disability, and disease (Finch and Kirkwood 2000; Kirkwood 2012). Moreover, SDV is suspected to account for the non-uniform response of the diseased cells of an organ to pharmaceuticals, which impedes medication (Niepel et al. 2009).
A better understanding of the causes and mediation of SDV could facilitate the fight against pathogens and the therapy of non-infectious diseases. It may help to reduce the fraction of non-responding pathogens and to sensitize higher percentages of diseased cells to therapeutic intervention. Proposals on the reduction of SDV were made by Heithoff et al. (1999), Davey Smith (2011), Miller-Jensen et al. (2013), and Pujadas and Feinberg (2012). For example, Miller-Jensen et al. (2011) studied how chromatin modifications modulate gene expression noise and argued that this fundamental information can be applied to develop innovative therapies against ‘pathogenic noise’. Heithoff et al. (1999) presented evidence for an essential role of stochastic DNA adenine methylation in virulence of salmonellae and suggested DNA adenine methylase inhibitors as therapeutic agents.
9 Implications of SDV for ecology
The consequences of SDV for genotype-to-phenotype mapping, individuality, and applied biology and medicine discussed in the previous sections were based on sound experimental evidence. The ecological and evolutionary implications of SDV outlined in the following are inferred with some plausibility from laboratory experiments and are exemplified by clonal populations. In sexual populations and in the complexity of natural habitats, it is difficult to keep genetic variation, environmental variation and SDV apart. In this section, I will discuss the role of SDV as a bet-hedging strategy in fluctuating environments, as an epigenetic chance generator and source of functional biodiversity, and as a facilitator of social interactions in clonal groups.
9.1 SDV as a bet-hedging strategy to cope with environmental uncertainties
Animals in the wild are often confronted with changing environments. They cope with such challenges either by the a priori production of phenotypes without knowing the future conditions (bet-hedging) or by the a posteriori alteration of phenotypes in response to environmental signals. Populations apply the latter strategy when there are reliable informational cues about the long-term state of the environment and the former strategy in unpredictable environments that lack such cues (Beaumont et al. 2009; Childs et al. 2010; Libby and Rainey 2011). Bet-hedging is a risk spreading strategy that enhances the probability of survival at the expense of a lowered arithmetic mean fitness (Ripa et al. 2010; Starrfelt and Kokko 2012). It is effectuated by genetic recombination, random mutation, or SDV. Genetic recombination is particularly important in sexually reproducing populations and SDV in asexual populations (Barton and Charlesworth 1998; Thattai and van Oudenaarden 2004; Acar et al. 2008; Veening et al. 2008). López-Garrido et al. (2012) emphasized that epigenetic bet-hedging (SDV) may be less risky for organisms than random mutation because increased mutation rates may involve a burden incompatible with adaptation.
At first glance, SDV related bet-hedging seems to be of minor significance in ecology because the asexuals as the main benefiters are apparently outnumbered by the sexually reproducing species. However, in bacteria and protists, asexual reproduction is the rule. Fungi and plants mostly combine sexual and clonal reproduction and the balance between the two varies widely between and within species (Taylor et al. 1999; Eckert 2002). For example, clonal or vegetative reproduction was found in 66.5% of the central European vascular plants, and in 112 vegetation types investigated, the frequency of clonal plants exceeded that of non-clonals (De Kroon and van Groenendael 1997). In animals, asexual reproduction is common in several higher taxa including the sessile sponges, cnidarians, bryozoans and ascidians and the parasitic trematodes and cestodes. Moreover, agricultural pests like the aphids and key species of aquatic food chains like the daphnids often reproduce by facultative parthenogenesis (Suomalainen et al. 1987; Vrijenhoek 1998; De Meeûs et al. 2007; Schön et al. 2009).
SDV might also play an important role in the invasion of new environments because invasive populations are often small and genetically impoverished. However, despite of these constraints they can be ecologically and evolutionarily very successful (Tsutsui et al. 2000). This phenomenon is well known as ‘invasion paradox’ but the underlying mechanisms are only poorly understood (Fridley et al. 2007). Invasive species are regarded as a major factor for the loss of biodiversity and modern environmental damages (Davis 2009). For example, in the United States, UK, Australia, South Africa, India, and Brazil together, alien plants, animals, and microbes were estimated to cause costs in damages and control of more than US$ 300 billion per year (Pimentel et al. 2001). Therefore, it is of prime importance to understand how invading species generate enough phenotypic variation to survive in new geographic areas and to extend ranges (Sakai et al. 2001; Davis 2009). The first steps of invasion, survival of the invading specimens and establishment of a founder population, seem to depend on epigenetic rather than genetic variation (Riis et al. 2010; Liebl et al. 2013), and SDV may be an important contributor to this epigenetic variation.
9.2 SDV as an epigenetic chance generator and source of functional biodiversity
The structure and dynamics of populations, the central units of ecology and evolution, are determined by the genetic chance generators random mutation, recombination, and drift, among others (Falconer and Mackay 1996; Lande et al. 2003; Beatty 2006, 2010; Lenormand et al. 2009; Koonin 2012). Studies on clonal populations in the laboratory and human twins revealed that there is a further epigenetic-based chance generator, namely SDV (Finch and Kirkwood 2000; Fraga et al. 2005; Vogt et al. 2008; Martin 2009; Czyz et al. 2012). SDV is probably effective in all populations but is particularly relevant when genetic variation is absent as in asexual populations or rather ineffective as in bottlenecked and small invasive populations.
Two examples of the marbled crayfish may give an idea on the influence of SDV on population structure and dynamics. In the first experiment, seven isogenic batchmates were reared communally for more than 550 days, and growth and egg-laying were regularly monitored. SDV of these life history traits caused repeated relative position changes of specimens within the group (figure 9A). For example, regarding total length, specimen S5 was number 5 at day 258, number 1 at day 365 and number 2 at day 558. In the second experiment, stage-3 batchmates from the same mother were divided into two groups of 10 and then raised under identical conditions for 312 days. Interestingly, the spectra of growth variation developed differently in the two groups. At the beginning of the experiment, CVs of carapace length were similar (2.92% versus 3.23%), diverged maximally at day 155 (26.7% versus 6.70%) and converged again until day 312 (24.6% versus 12.8%) (figure 9B).
SDV as an epigenetic chance generator in cultured populations of marbled crayfish. (A) SDV-caused position changes of individuals in a communally raised population of seven batchmates (S1–S7). Individual profiles of growth and reproduction were monitored for more than 300 days. Red vertical bars indicate time of oviposition (from Vogt et al. 2008). (B) Differences of SDV of growth in subpopulations from the same batch. Stage-3 juveniles were removed from their mother, randomly divided into two groups of ten, and then raised under identical conditions for 300 days. Note fluctuation of CV of carapace length (CL) over time in each group and differences in dimension and fluctuation pattern between groups. Figures on bars indicate number of specimens (from Vogt et al. 2008).
SDV affects all kinds of traits but only some of them are ecologically and evolutionarily important fitness traits. The difficulty to discriminate between fitness traits and non-fitness traits has repeatedly been discussed (Mills and Beatty 1979; Ariew and Lewontin 2004), but at least camouflage, longevity, and the number of offspring per female can be regarded as fitness traits. These traits were shown to vary markedly among batchmates of marbled crayfish (table 2; figure 5E). In the case of camouflage, the fitness trait is probably marmoration itself and not the detailed arrangements of the pigment spots. This interpretation is supported by information on coat coloration in cats and dogs, which possess genes for solid coat, piebald spotting, and so forth, but no genetic instruction for the spatial distribution of individual motifs (Kaelin and Barsh 2013). The high degree of SDV in colour patterns, fingerprints, noseprints, and irises may be explained by assuming that these traits have neither adaptive nor maladaptive values and that the cost for their buffering is saved. SDV generates fitness variation also in unicellular organisms as shown by Wang and Zhang (2011) for stochastic gene expression in yeast.
According to common belief, it does not matter which of the members of an asexual population reproduces because all individuals are expected to yield the same progeny. This assumption would only be correct if the genes were the sole determinants of phenotype. However, if the clonal reproducers were epigenetically and phenotypically diverse due to SDV, then the future structure and performance of a population would very much depend on which specimens contribute to the next generation, resembling the situation in sexually reproducing populations.
SDV can create ‘functional biodiversity’ via epigenetic variation and can thus be an important driver of population and ecosystem functioning similar to environmentally induced epigenetic variation (Smith et al. 2011; Latzel et al. 2013; Medrano et al. 2014). There is experimental evidence that SDV can modify factors such as fitness and biomass, at least in clonal populations. For example, Smith et al. (2011) investigated the influence of SDV on 12 leaf traits in eight natural clones of quaking aspen, Populus tremuloides, over the 160-year life span and found increases of phenotypic diversity with age. They concluded that this developmental source of phenotypic variation might enhance clonal fitness and partially underlie aspen’s ability to tolerate the large environmental gradients in its broad geographical range. Latzel et al. (2013) performed greenhouse experiments with epigenetically diverse populations of Arabidopsis thaliana and found that they produce up to 40% more biomass than epigenetically uniform populations. The positive effects of epigenetic diversity were strongest when populations were grown together with competitors or infected with pathogens.
The importance of SDV or environmentally induced epigenetic differences as a source of phenotypic diversity in sexually reproducing populations has recently been demonstrated by Medrano et al. (2014). These authors investigated the relationship between epigenetic variation and functional plant diversity by conducting epigenetic and genetic marker-trait association analyses for 20 traits in the perennial herb Helleborus foetidus from 10 sampling sites in southeastern Spain. They revealed greater epigenetic than genetic diversity and concluded from their results that epigenetic variation can be an important source of intraspecific functional diversity, facilitating exploitation of a broader range of ecological conditions.
9.3 SDV as a facilitator of social interactions in clonal populations
Despite genetic identity, parthenogenetic animals can establish social hierarchies as shown for geckos, lizards, and marbled crayfish (Case 1990; Bolger and Case 1992; Vogt et al. 2008; Farca Luna et al. 2009). Establishment and maintenance of social hierarchies requires individual recognition of group members either by optical or chemical cues (Wilson 1975). Such individual signatures can obviously be generated by SDV as exemplified by marbled crayfish, which have individual spotting patterns and probably individual urine signatures. The latter indicate the status of aggressiveness in crayfish and are respected by conspecifics (Breithaupt and Eger 2002).
In some clonal populations of microorganisms, SDV can generate different phenotypes that perform diverse functions for the benefit of the population, for example during pathogenesis or in biofilms. Ackermann et al. (2008) established that in pathogenic bacteria SDV can promote the formation of a subpopulation that releases enzymes by lysis, preparing the ground for a successful infection. The remaining bacteria benefit from this self-destructive cooperation, invade the host, and proliferate. This cooperative strategy differs from bet-hedging with respect to the benefits for the population: in bet-hedging, the benefits arise when the environment fluctuates, whereas in social cooperation the benefits come from the enhancement of fitness and the gain of new functionality (Avery 2006; Ackermann 2013).
10 Implications of SDV for evolution
Even if considered as a one-generation phenomenon only, SDV would have some evolutionary consequences. By providing a range of phenotypes from the same genotype, it enhances the chance to survive adverse conditions and to perpetuate evolution. However, the evolutionary relevance of SDV would be far greater if the underlying epigenetic signatures would be heritable, selectable, and evolvable under certain circumstances.
10.1 Inheritance, reinforcement, and genetic solidification of SDV-induced epigenetic patterns
The transgenerational inheritance of epigenetic patterns, either induced by SDV or environmental cues, is not yet generally accepted. This is in contrast to the mitotic inheritance of epigenetic signatures that ensures the establishment of morphologically and functionally diverse tissues and organs from the zygote (Ringrose and Paro 2007; Probst et al. 2009). The idea of transgenerational epigenetic inheritance and its role in evolution was first raised by Waddington (1942) and repeatedly reviewed in recent years (Jablonka and Raz 2009; Hauser et al. 2011; Pfennig and Servedio 2013). There are examples published for bacteria, plants, and animals (Henderson and Jacobsen 2007; Adam et al. 2008; Kovalchuk 2012), suggesting that this phenomenon is widespread and that its mechanisms may have been selected for during evolution (Jablonka 2012; Lim and Brunet 2013). The inherited state often shows incomplete penetrance, which means that only a proportion of the offspring is affected (Daxinger and Whitelaw 2012).
The transmission of epigenetic patterns to the next generation is quite plausible in bacteria and protists, which reproduce by binary fission. In plants, there is also great chance for epigenetic markers of soma cells to make their way into vegetative propagules or germ cells because plants often reproduce asexually and separate soma and germline late in development (Saze 2008). In animals, the situation is more complex. Following Weismannʼs germ plasm theory, mutations and epimutations of the soma cannot be transmitted to the germ cells and are therefore not heritable, as discussed in detail in Haig (2007) and Jablonka and Raz (2009). However, this constraint was inferred from mammals and does not apply to all animal groups. In nematodes, insects, and vertebrates, there is indeed an early separation of the germline from the soma (Extavour and Akam 2003), but in sponges, cnidarians, bryozoans, flatworms, annelids, ascidians, and echinoderms, somatic cells can transform into germ cells in later life stages. Moreover, in these taxa, soma cells are also used to form new individuals by budding and fragmentation (De Meeûs et al. 2007; Schön et al. 2009), and thus, the transmission of epigenetic profiles to the next generation is easily understandable.
Sharma (2013) and Gapp et al. (2014) even raised the possibility of a soma-to-germline communication that might cause epigenetic modifications in the germ cells indirectly through modifications affecting primarily the soma. There is evidence for such a communication in plants and mouse by mobile regulatory RNAs and hormones. In animals, the accessibility of germ cells for blood-borne signals depends very much on taxon and the developmental stage of the gonads. For example, when the ovary is beginning to be formed in hatchlings of marbled crayfish the oogonia have direct contact to the haemolymph and are accessible for epimutagenic substances like the neighbouring tissues (figure 10A). The vitellogenic oocytes, in contrast, are separated from the haemolymph by follicle cells (figure 10B), which may keep such substances away.
Accessibility of germ cells of marbled crayfish for epimutagenic signals. (A) Primordium of ovary in hatchling showing oogonium (o) with large nucleus (n) and surrounding matrix cells (arrowhead). The oogonium has direct contact (arrow) to the thoracic haemal sinus (h) and is thus equally accessible for blood-borne epimutagenic substances as the neighbouring hepatopancreas cells (he) (from Vogt 2015). (B) Vitellogenic oocyte being completely separated from the haemal sinuses by a capsule of follicle cells (arrow), which acts as a filter for blood-borne substances (from Vogt et al. 2004).
In order to have long-term evolutionary consequences, epigenetic profiles generated by SDV and inherited to the next generation must be stabilized, reinforced, and eventually genetically solidified. Optimally, only those epigenetic patterns should be preserved and selected that accomplish adaptation to new environmental conditions not experienced by the parental generation (Hirsch et al. 2012). Herman et al. (2014) emphasized that the stability of epigenetic states beyond a single generation depends on the degree of environmental variation as well as on the costs of epigenetic resetting. Support for this view comes from Becker et al. (2011), who investigated spontaneous epimutations in 10 lineages of Arabidopsis thaliana over 30 generations under constant greenhouse conditions. They found that under these constant conditions, not many epimutations were stably inherited over the long term and that some sites seemed to go through recurrent cycles of forward and reverse epimutation.
Reinforcement of epigenetic profiles in new environments may be achieved by the interaction of SDV with phenotypic plasticity, the directional influence of environmental cues on epigenetic signatures (Scheiner 1993; Ghalambor et al. 2007). SDV and phenotypic plasticity are different epigenetic sources of phenotypic variation by definition but are coupled to some degree. SDV produces phenotypes a priori around a mean target phenotype, whereas phenotypic plasticity produces phenotypes a posteriori as the result of environmental cues, shifting the target phenotype to new optima. Both have in common that they can be mediated by DNA methylation. There is some more information on the mechanisms of coupling with respect to gene expression of yeast and bacteria. In yeast, coupling was found to be particularly strong for genes with specific promoter architecture (TATA box and high nucleosome occupancy) but weak for genes in which high noise may be detrimental (Lehner 2010). In Escherichia coli, coupling was influenced by the level of gene expression, dosage sensitivity of genes, and regulation by specific nucleoid-associated proteins and transcription factors (Singh 2013).
Zhang et al. (2013) established by glasshouse experiments that variation in DNA methylation can cause substantial heritable variation of ecologically important traits in Arabidopsis thaliana and concluded that epigenetic variation may speed up evolution. Cortijo et al. (2014) quantified the impact of heritable epigenetic variation on complex traits in isogenic Arabidopsis lines that carried experimentally induced DNA methylation changes at hundreds of regions across the genome. They demonstrated that several of these differentially methylated regions act as bona fide epigenetic quantitative trait loci, accounting for 60% to 90% of the heritability of flowering time and primary root length. These epigenetic quantitative trait loci can be subjected to artificial selection. Many of the experimentally induced differentially methylated regions also vary in natural Arabidopsis populations and may thus provide an epigenetic basis for Darwinian evolution.
The genetic solidification of epigenetically mediated phenotypes is often explained by genetic accomodation, genetic assimilation, and the Baldwin effect (Nanjundiah 2003; West-Eberhard 2003; Pigliucci et al. 2006; Crispo 2007; Angers et al. 2010). These mechanisms were proposed by West-Eberhard (2003), Waddington (1953), and Baldwin (1896), respectively, and explain how phenotypes originally produced in response to an environmental stimulus later become genetically encoded by artificial or natural selection. Genetic assimilation requires pre-existing genetic variability, whereas the Baldwin effect does not making it more suitable to explain genetic solidification of epigenetic variation in clonal populations (Nanjundiah 2003).
In the following, I would like to present three further ideas on the conversion of epigenetic variation into genetic variation, namely, facilitated genetic mutation in methylated DNA regions, activation of transposons by demethylation, and methylation dependent expression, duplication and elimination of copy number variants. Interestingly, methylated DNA regions have a 10- to 50-fold higher mutation rate than unmethylated regions, which might lead to targeted DNA sequence divergence. Methylated CpG sites can mutate to TpG and to CpA in the complementary strand (Elango et al. 2008; Hunt et al. 2010; Xia et al. 2012). In honeybee, genes with high CpG content, i.e. higher probabilities of genetic mutation, are associated with developmental processes, whereas genes with low CpG content are associated with basic biological functions (Elango et al. 2008). Transposons are often silenced by DNA methylation. If activated by demethylation they can become powerful mutagens generating genetic diversity (Weigel and Colot 2012).
Gene duplication is recognized to play a fundamental role in the origin of phenotypic diversity and is sometimes regarded as a major evolutionary path towards the gain of new function and the origin of new species (Lynch and Conery 2000; Taylor and Raes 2004; Roth et al. 2007). Gene duplicates originate by errors in homologous recombination and by retrotransposition events and account for 8–20% of the genes in eukaryotic genomes, with extremes of 90% in some plants (Moore and Purugganan 2005). The scenario in figure 11 starts from the DNA sequence of a parthenogenetic mother that includes slightly different copy number variants of a gene, one of them being active and the others being silenced by DNA methylation. In the next generation, the pattern of active and silenced duplicates is stochastically changed, resulting in genetically identical but epigenetically and phenotypically different batchmates. In the following generations and under the influence of different stabilizing environmental cues, long-term activated gene variants may be duplicated and some of the long-term silenced variants may be eliminated, resulting in lineages that differ from each other epigenetically and genetically. To date, there is no experimental evidence for such a mechanism but it is well known that genes duplicate quite often, that the activation status of copy number variants can change and that they can disappear from the genome (Rodin and Riggs 2003; Roth et al. 2007).
Hypothetical scenario on the transformation of epigenetic diversity into genetic diversity. (A) Starting point is a piece of DNA of a parthenogenetic female that includes four copy variants of a gene (a1–a4) with slightly different DNA sequences. Only one of these duplicates is active and three are silenced by DNA methylation. (B) The progeny are genetically identical with their mother and among each other but differ epigenetically due to stochastic activation and silencing of gene copies. (C) In the following generations the epigenetic patterns are stabilized under the influence of different environments (E1–E3). Finally, the epigenetic diversity is transformed into genetic diversity by the duplication of long-term active gene variants and the elimination of long-term silenced variants.
The sketched scenario of epigenetic diversification of a genetically uniform population and transformation of this epigenetic diversity into genetic diversity seems to be particularly important for the perpetuation and evolution of asexual lineages (Castonguay and Angers 2012; Verhoeven and Preite 2014). It may also be important for the radiation of small invasive groups in new environments (Dybdahl and Kane 2005; Ghalambor et al. 2007). The chance of the invasive generation to survive is certainly enhanced by the a priori provisioning of phenotypic variants via SDV. Differences in the preference of food, substrate, or temperature by the various phenotypes over generations may then generate diverse ecotypes, and genetic solidification of the epigenetic differences may finally lead to lineage splitting or even speciation.
10.2 SDV as a novel evolution factor
SDV can only be regarded as an evolution factor if at least some of its aspects are heritable, adaptable, selectable, and evolvable. All of these features have convincingly been demonstrated for stochastic gene expression in cultured bacteria and yeast (Ackermann 2013; Viney and Reece 2013). In multicellular organisms, the situation is more complex because some aspects of SDV may meet these requirements, for instance those mediated by DNA methylation, whereas others like those mediated by mechanical forces may not.
Viney and Reece (2013) presented evidence that phenotypic differences produced by stochastic gene expression in microorganisms can have fitness advantages suggesting that evolution can shape SDV. Fraser and Kærn (2009) discussed different scenarios where stochastic gene expression might bestow a selective advantage, highlighting a potential role of SDV for the evolution of microbial survival strategies. Based on empirical results and theoretical models, Zhang et al. (2009) suggested that for some yeast genes such as plasma-membrane transporters elevated levels of expression stochasticity are advantageous, are subject to positive selection, and facilitate the evolution of adaptive gene expression. Kaneko and Furusawa (2008) concluded from their work that growing cells have a general ability for adaptation by taking advantage of stochasticity in gene expression and emphasized its importance for the adaptation of microorganisms to different environments.
There is also evidence that selection can act on SDV-produced phenotypes in isogenic animal populations. Gärtner (1985, 1990) revealed SDV-caused Gaussian distributions for body weight in populations of inbred mice and rats and assumed that such distributions would be ideal for natural selection to act upon, resembling the Gaussian curves of sexually reproducing populations and the norm of reaction curves (Falconer and Mackay 1996; Schlichting and Pigliucci 1998). Experiments with inbred mice and rats in a selection-neutral laboratory environment indeed demonstrated the presence of sexual selection despite genetic uniformity. Males had the highest chance to parent the next generation when coming from the central classes around the mean body weight.
Evolvability is the capacity of a developmental system to adapt to selective pressures, which is largely a function of the system’s ability to generate heritable phenotypic variation (Hendrikse et al. 2007). It has been convincingly demonstrated for stochastic gene expression in bacteria, yeast, and eukaryotic cell lines (Fraser et al. 2004; Kaneko and Furusawa 2008; Neildez-Nguyen et al. 2008; Viney and Reece 2013). In multicellular organisms, the various SDV generators and the buffering systems that limit them have genetic components as discussed earlier, and therefore, SDV should be evolvable in these organisms, too.
These examples apparently support the idea of SDV as an important, hitherto neglected evolution factor. It might add another interesting facet to the emerging Extended Synthesis, which attempts to integrate phenotype-based concepts and epigenetics into neo-Darwinian Modern Synthesis (Müller and Newman 2003; West-Eberhard 2003; Pigliucci and Müller 2010; Hallgrímsson and Hall 2011a; Jablonka and Lamb 2014).
10.3 Evidence for an evolutionary role of SDV under natural conditions
So far, I have reflected on evolutionary implications of SDV on the basis of laboratory experiments with clonal organisms. Evidence for the proposed evolutionary role of SDV in natural populations is weak, mainly because of the difficulty to distinguish between genetic, stochastic developmental, and environmental sources of phenotypic variation in the wild. However, there are some hints for the existence and effectiveness of SDV in nature coming from bet-hedging and social cooperation in environmental microorganisms, the radiation of obligatory parthenogenetic animal taxa, and the evolutionary success of bottlenecked and invasive populations.
SDV-related bet-hedging and social cooperation of microorganisms have been observed not only in the laboratory but also in nature. Using single-cell analysis, Yvert et al. (2013) profiled hundreds of quantitative traits in 37 natural strains of Saccharomyces cerevisiae from different geographical regions and ecological origin. They demonstrated trait specificity and quantitative differences between natural populations in ‘phenotypic noise’, supporting the possibility that microevolution might tune SDV in the wild. Nikel et al. (2014) concluded from their analysis of biodegradation of aromatic compounds in the saprotrophic soil bacterium Pseudomonas putida that SDV-related bet-hedging and social cooperation are important factors besides genetic mutation in pushing forward the evolution of environmental microorganisms. Further examples of SDV-related phenotypic differences in natural clonal lineages are summarized in Richard and Yvert (2014).
According to traditional belief, clonal lineages are dead ends of evolution because of the absence of genetic recombination, the most effective mechanism to create new phenotypes. Hence, higher taxa of obligate parthenogens should not exist. However, the bdelloid rotifers that lived without sex for about 40 million years as convincingly proven by Mark Welch and Meselson (2000) yielded 4 families, 18 genera, and 360 species. A similar diversity evolved in some obligate parthenogenetic freshwater ostracod groups, which exist since more than 100 million years without sex (Butlin et al. 1998). The diversity of such evolutionarily successful asexuals is usually explained with random mutation, the temporal appearance of sexually reproducing individuals, the separate origins of clones from sexual ancestors, and hybridization between asexual females and males from related sexual species, but all of these arguments are purely hypothetical. An alternative explanation might be clonal splitting by the interaction of SDV, phenotypic plasticity, epigenetic inheritance, and genetic solidification as outlined above.
Another hint for the effectiveness of SDV in the wild comes from the evolutionary success of genetically bottlenecked populations and small invasive groups. SDV may help to survive the bottleneck and the invasive period, facilitating further evolution. A good example for an evolutionary very successful bottlenecked species is modern humans, which went through one or more bottlenecks of a few thousand individuals in the Pleistocene (Ambrose 1998) but have yielded a broad range of phenotypically different ethnicities since then. The effectiveness of SDV in biological invasions may be best illustrated by the marbled crayfish of Madagascar. In about 2005, somebody released one or a few individuals of this crayfish near the capital of Madagascar. Thereafter, this exotic multiplied in an enormous speed and invaded habitats diverse as rice paddies, rivers, lakes, and swamps in eight of the country’s 22 regions (Jones et al. 2009). SDV has probably facilitated survival of the first generation and the establishment of founder populations in the different habitats.
10.4 Origin of SDV and relative importance in different evolutionary periods
There are essentially three chance generators that produce phenotypic variants without knowing the future conditions, namely random mutation, meiotic recombination, and SDV. Random mutation is thought to date back to the beginning of life, whereas meiotic recombination originated much later in context with sexual reproduction. SDV evolved at different times, depending on specification. Due to its occurrence in all kingdoms of life, inter-individual SDV probably dates back to the first cellular organisms that appeared some 3.5 billion years ago (Cavalier-Smith 2006) or even to a possibly preceding virus world (Koonin et al. 2006). Fluctuating asymmetry and variation among homonomous metamers emerged much later in the context of multicellularity. Variation among homonomous metamers evolved in algae, animals, and vascular plants some 1200, 530, and 430 million years ago, respectively (Kenrick and Crane 1997; Butterfield 2000; Couso 2009). Fluctuating asymmetry appeared together with bilateral symmetry at about 600 Mya in animals and 430 Mya in vascular plants (Finnerty et al. 2004).
The relative importance of random mutation, meiotic recombination, and SDV for the a priori production of phenotypic variants has supposedly changed during evolution depending on cell and body architecture, mode of reproduction, and life histories of the prevailing taxa. In the first 1.7 billion years of life, organisms were exclusively prokaryotic and reproduced asexually. Prokaryotes are thought to generate phenotypic variation mainly by random mutation and to a lesser degree by recombination and horizontal gene transfer (Denamur and Matic 2006; Lawrence and Retchless 2009). The involvement of SDV in this process has been recognized in recent years in connection with stochastic gene expression, but its share in the a priori generation of phenotypic variation in prokaryotes is not yet established.
The eukaryotic cells appeared on the scene at about 1800 Mya. The first eukaryotes were asexually reproducing protists (Knoll et al. 2006). Most of the extant protists still reproduce asexually with rare interspersed sex (Dacks and Roger 1999). For instance, in the ichthyosporean Pseudoperkinsus tapetus, 1 sexual cycle for every 22,700 asexual cycles was determined (Marshall and Berbee 2010). The origin of sexual reproduction and concomitant meiotic recombination is obscure. It is dated to between 1700 and 850 Mya (Zhu and Chen 1995; Cavalier-Smith 2010). The predominance of asexual reproduction and the considerable augmentation of stochasticity generating mechanisms in protists compared to bacteria suggest that the relative importance of SDV may have increased in the ‘age of the protists’.
Sexual reproduction is the rule in multicellular animals and plants, which dominate the picture of modern ecosystems (Bell 1982). Therefore, the relevance of meiotic recombination in the production of phenotypic diversity is assumed to have dramatically increased since the Cambrian radiation of the Animalia and the conquest of land by the vascular plants. The relative role of SDV has probably decreased in this time period despite the emergence of new stochasticity generators. However, for all asexually reproducing taxa and genetically impoverished populations SDV is still of prime importance.
11 Problems, open questions, and some future research directions
Among animal biologists, there is no consensus on whether SDV is real or just an experimental artefact as discussed in Larsen (2005) and Oey and Whitelaw (2012). Cell biologists and microbiologists are one step ahead because at least stochastic gene expression is widely accepted. The arguments made against SDV in animals mainly result from the difficulty to demarcate it from the genetic and environmental proportions of phenotypic variation. The indoor experiments with isogenic animals reviewed in this article strongly suggest that SDV is real and a source of phenotypic variation in its own right. The most impressive examples of the impact of SDV on phenotype are spotted coat coloration and wiring of the brain. It seems inherently unlikely that the pigmentation of each of the billions of hair follicles in cattle is determined by a specific genetic instruction (Seidel et al. 2003) and that the human brain with its 1015 synapses is genetically specified down to the finest detail (Clarke 2012).
Theoretically, SDV is the phenotypic variation that remains after subtraction of genetic variation and environmental variation from total phenotypic variation. In reality, however, measured SDV may be curtailed to some degree by individual genetic mutations and non-shared micro-environmental influences. These factors largely elude detection and measurement and are therefore often urged as arguments against SDV. Genetic differences of clonemates can come from mutations in the germline of the parent or from somatic mutations in the clonemates. Recent whole-genome sequencing revealed random mutation values per haploid genome per generation of 0.001, 0.99, 2.9 and 87.5 in Escherichia coli, Drosophila melanogaster, Caenorhabditis elegans and Homo sapiens, respectively (Lynch et al. 2008; The 1000 Genomes Project Consortium 2010; Lee et al. 2012), suggesting that germline mutations do not markedly contibute to phenotypic diversity of the offspring.
The potential of somatic mutations for curtailing putative SDV is much dependent on the number of soma cells in an organism and the corresponding frequency of cell division (Favor and Neuhäuser-Klaus 1994; Gill et al. 1995). In the nematode Caenorhabditis elegans, in which 1090 cells form the adult body, somatic mutations are largely irrelevant. Humans, in contrast, have approximately 1013 to 1014 cells and a correspondingly higher frequency of cell division suggesting that most human cells carry at least one spontaneous mutation (Frank 2010). Comparison of the whole genome of blood cells between human monozygotic twins indeed revealed differences in single nucleotide polymorphisms and copy-number variants (Bruder et al. 2008; Maiti et al. 2011), demonstrating that organisms regarded as genetically identical display some genetic differences, indeed. Therefore, in long-lived organisms with large body sizes like cattle and humans, somatic mutations may curtail SDV to some degree (Biesecker and Spinner 2013).
Micro-environmental influences like local differences in light and temperature are certainly relevant for populations in the wild, particularly for plants and sessile animals. However, in simple laboratory settings like those used for the culture of Caenorhabditis, Drosophila, Daphnia, marbled crayfish, and mice and rats, such influences can be reduced to almost zero. Larger mammals like pigs and cattle require higher diversification of facilities and food, leaving more room for speculations on the curtailing effect of non-shared environment on SDV. This holds even more for human monozygotic twins. In mammals, micro-environmental parameters of the uterus were sometimes assumed to considerably influence phenotypic variation of twins. However, transplantation experiments with artificially prepared calf twins revealed that the uterine environment contributes only a few percent to the variation of size and body weight in later life (Gärtner et al. 1991).
There are still many open questions on SDV, which require more intense investigation, particularly with respect to animals and plants. Among them are the mediation of SDV by molecular epigenetic mechanisms, the relationship between SDV and phenotypic plasticity in natural environments, the transgenerational inheritance of epigenetic patterns, the conversion of epigenetic differences into genetic differences, and the experimental manipulation of SDV. The availability of SDV-models with different features and the application of modern genomic, epigenomic and postgenomic techniques may help to answer these questions.
The concept of transgenerational epigenetic inheritance is of fundamental importance to understand how epigenetic signatures spread across generations, no matter whether they mediate SDV or phenotypic plasticity (Lim and Brunet 2013; Pfennig and Servedio 2013). Although there are some good examples from different organisms, this type of information transfer is not yet generally accepted. A major problem is that most experiments were run over 2 to 3 generations only. Further research is needed to clarify, how and under what conditions epigenetic marks are inherited and under what conditions they persist. These questions are presently under investigation in several laboratories (Ashe et al. 2012; Skinner et al. 2012; Turck and Coupland 2014). The genetic solidification of epigenetically mediated phenotypic differences is even more contentious. Although it would make rapid evolutionary changes more plausible, it remains to be elucidated if mechanisms like genetic assimilation or the Baldwin effect really exist, how they act, and how often they are involved in evolutionary change (Braendle and Flatt 2006; Schlichting and Wund 2014).
The proposed scenario on lineage splitting and speciation by the interaction of SDV, transgenerational epigenetic inheritance, phenotypic plasticity, and genetic solidification of epigenetic differences could principally be proven by exposing genetically identical but phenotypically different cohorts of batchmates to strikingly different environments and monitoring sensitive epigenetic and genetic markers over many generations. Alternatively, one could compare the genomes and epigenomes of populations from different natural habitats that are known to have evolved from a single introduced clone. A good example is the marbled crayfish of Madagascar, which has generated different ecotypes from a single clone within 10 years (Jones et al. 2009). Another interesting example is the obligatory parthenogenetic American clone of Daphnia pulex that was introduced into Lake Naivasha (Kenya) in 1927. This clone has completely displaced native Daphnia pulex from Ethiopia to South Africa (Mergeay et al. 2006). Interestingly, it would not only allow comparison of epigenetic and genetic markers of populations from different habitats but also of earlier generations that have left behind dormant eggs in subsequent sediment layers.
One of the reasons for the low interest of researchers in SDV may result from its chance character that apparently prohibits forecasting and manipulation. However, at least some aspects of SDV can be manipulated, for instance, by influencing the buffering system via Hsp90 and microRNAs (Li et al. 2010; Kim and Sauro 2012). Another promising approach to modify SDV is drug interference with molecular epigenetic mediators like DNA methylation and histone acetylation (Ho et al. 2013; Fischer 2014).
Numerous authors have written on SDV before me and some of them have presented profound arguments on its biological relevance. In this article, I have summarized these works, added my own experimental data and ideas, and endeavoured to conceptualize SDV across all kingdoms of life and major fields of biology. Of course, this review is not the final truth. Rather, it is hoped to provide a framework for fruitful discussions among experts and help to design future experiments that are more target-oriented. Further refinement of this SDV-concept requires the input of colleagues from various experimental and theoretical biological disciplines. It was beyond the scope of this article to intensely discuss the consequences of SDV for human life, but psychologists and social scientists may pick up the ball.
12 Conclusions
(1) Experiments with genetically identical organisms in highly standardized laboratory environments clearly revealed that there is a third source of phenotypic variation besides genetic variation and environmental variation, namely, stochastic developmental variation. Although known for more than a century, this source of phenotypic variation has not made its way into biological theory, probably because of its unpredictable character and the difficulty to demarcate it from genetic and environmental variation in the wild. Meanwhile, there are several suitable model organisms available to investigate SDV in detail.
(2) Studies on organisms diverse as archaeans, bacteria, protists, fungi, plants, and animals suggest that SDV is a universal and phylogenetically old principle, which serves for the a priori production of phenotypic variants around a well-adapted target phenotype. There are considerable differences in SDV generation among the supergroups of life depending on cell architecture, body plan, and life style.
(3) SDV can be produced at all levels of biological organization from molecule to organism. It results from stochastic cellular processes, nonlinear chemical and mechanical mechanisms of patterning and organogenesis, and probabilistic self-reinforcing circuitries in the adult life. It is mediated by molecular epigenetic mechanisms like DNA methylation and higher-level epigenetic mechanisms like self-organization within developing tissues.
(4) SDV was shown to vary considerably among traits and trait categories. In animals, it is mostly relatively small for morphometric characters, larger for biochemical and physiological traits and largest for behavioural and life history traits. Some characters like fingerprints and spotted coloration of the integument have an extremely broad range of SDV. Inter-individual SDV and intra-individual fluctuating asymmetry are different outcomes of developmental stochasticity and are not narrowly correlated.
(5) It is commonly believed that in a particular environment a single DNA-sequence and its copies map to one phenotype only. However, thanks to SDV, genotype-to-phenotype mapping is one to many even under this condition generating individuality in clonal populations. This individualizing effect of SDV curtails the dream of producing organisms with identical properties for human benefit by inbreeding or artificial cloning.
(6) SDV has been feared for long as an obstacle in the standardization of test animals and breeding of true-to-type plants. On the other side, SDV could be exploited for the improvement of crop and microbial strains for biotechnology. In medicine, SDV receives increasing attention as a modifier of the infectivity and resistance of pathogens, which are mostly asexual organisms. There are first ideas on how to manipulate the dimensions of SDV, either to reduce its nuisance effects or to enhance its beneficial properties.
(7) The production of different phenotypes from the same genotype by SDV is a bet-hedging strategy that enhances the chance to stay in the game of life when the environmental conditions change. This strategy is particularly advantageous for clonal and genetically impoverished populations. Further ecological implications of SDV include epigenetic chance generation in populations, the production of functional biodiversity, and facilitation of social interaction in clonal groups.
(8) There is no long-term experimental proof yet for SDV as an evolution factor. However, it seems plausible that phenotypes produced by SDV can serve as raw material for natural selection. The underlying epigenetic patterns may be inherited under certain conditions, reinforced by environmental cues, and genetically solidified, yielding different ecotypes or even species on the long term. Some aspects of SDV such as stochastic gene expression have been shown to be heritable, adaptable, selectable, and evolvable, supporting the idea of SDV as a hitherto overlooked evolution factor.
(9) SDV affects almost all aspects of life, either negatively as a nuisance or positively as a bet-hedging strategy, and therefore, it requires a higher level of attention and more adequate integration into biological theory. The current attempt to conceptualize SDV across the entire spectrum of organisms and trait categories is intended to stimulate discussion among experts from various disciplines in order to achieve this goal.
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Acknowledgements
The author is grateful to the following colleagues for providing pictures and information: Martin Ackermann (Zurich), Gregory S Archer (Davis), Gregory A Babitt (Rochester), Jürgen Berger and Ralf J Sommer (Tübingen), Brian Bagatto (Akron), John Daugman (Cambridge), Jan Dijksterhuis and Han Wösten (Utrecht), David Fenwick (Penzance), Theodore H Friend (College Station), Mikael Häggström (Uppsala), David S Haskell (Sewanee), John F Hasler (Bogart), Anil K Jain (East Lansing), Nikos C Kyrpides (Walnut Creek), Winfried Lampert (Plön), Eric C Lai (New York), David IK Martin (Oakland), Erskine L Palmer and Russell Regnery (Atlanta), Soo-Bong Park (Suwon), Jorge A Piedrahita (Raleigh), Pascal Radtke (Düsseldorf), George E Seidel Jr (Fort Collins), Michael R Strand (Athens), Stefano Tiozzo (Villefranche-sur-Mer), Rogier R van Vugt (Leiden), Larry Wadsworth and Mark Westhusin (College Station), and Alex Wild (Champaign-Urbana). Many thanks also to Vidyanand Nanjundiah (Bangalore) and an anonymous referee for constructive criticism and numerous suggestions that helped to improve the manuscript.
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[Vogt G 2015 Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences. J. Biosci. 40 1–46] DOI 10.1007/s12038-015-9506-8
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Vogt, G. Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences. J Biosci 40, 159–204 (2015). https://doi.org/10.1007/s12038-015-9506-8
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DOI: https://doi.org/10.1007/s12038-015-9506-8