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Phenotypic Plasticity: Molecular Mechanisms and Adaptive Significance

Scott A. Kelly, Tami M. Panhuis, Andrew M. Stoehr

10.1002/cphy.c110008

Source: Volume 2, Issue 2, April 2012

Published online: April 2012

Full Article on Wiley Online Library



Abstract

Phenotypic plasticity can be broadly defined as the ability of one genotype to produce more than one phenotype when exposed to different environments, as the modification of developmental events by the environment, or as the ability of an individual organism to alter its phenotype in response to changes in environmental conditions. Not surprisingly, the study of phenotypic plasticity is innately interdisciplinary and encompasses aspects of behavior, development, ecology, evolution, genetics, genomics, and multiple physiological systems at various levels of biological organization. From an ecological and evolutionary perspective, phenotypic plasticity may be a powerful means of adaptation and dramatic examples of phenotypic plasticity include predator avoidance, insect wing polymorphisms, the timing of metamorphosis in amphibians, osmoregulation in fishes, and alternative reproductive tactics in male vertebrates. From a human health perspective, documented examples of plasticity most commonly include the results of exercise, training, and/or dieting on human morphology and physiology. Regardless of the discipline, phenotypic plasticity has increasingly become the target of a plethora of investigations with the methodological approaches utilized ranging from the molecular to whole organsimal. In this article, we provide a brief historical outlook on phenotypic plasticity; examine its potential adaptive significance; emphasize recent molecular approaches that provide novel insight into underlying mechanisms, and highlight examples in fishes and insects. Finally, we highlight examples of phenotypic plasticity from a human health perspective and underscore the use of mouse models as a powerful tool in understanding the genetic architecture of phenotypic plasticity. © 2012 American Physiological Society. Compr Physiol 2:1417‐1439, 2012.

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Figure 1. Figure 1.

Two genetically identical water fleas, Daphnia lumholtzi. The helmet and extended tail spine of the individual on the left were induced as a result of chemical cues from a predaceous fish and serve as protection 67. This figure is recreated, with permission, from 3 Agrawal, A. “Phenotypic plasticity in the interactions and evolution of species”, Science, October 12, 2001, 294:321‐326, Figure 1, with permission of D. Laforsch.
Figure 2. Figure 2.

Possible relationships among plasticity and genetic variation. In each panel, dots connected by lines represent the phenotypes expressed by each of three genotypes (or families), numbered 1, 2, and 3, in each of two alternative environments (A, B). These lines are the reaction norms. (A) The three genotypes differ in their phenotypes within each environment, indicating genetic variation (VG = yes). However, a given genotype expresses the same phenotype, regardless of environment; that is, the reaction norms are flat. Hence, there is no environmental effect on the phenotype (VE, or plasticity, is absent). Because the reactions norms are parallel, there is no genotype‐by‐environment interaction (GxE = no). (B) As in panel “A,” the three genotypes differ in phenotype within a given environment, but in addition, each genotype expresses a different phenotype in environment “B,” relative to that expressed in environment “A.” That is, the reaction norms are not flat; each genotype is plastic for the trait of interest. However, because the slopes of all reaction norms are parallel, there is no genotype‐by‐environment interaction. The genotypes, although plastic, are all similarly plastic (for the trait of interest). (C) Genotypes differ within environments, show plasticity, and differ in plasticity. That is, the reaction norms are not parallel, indicating genotype‐by‐environment interactions. In this case, reaction norms 2 and 3 cross, but this may not always be the case. This figure is conceptually similar to, and derived, with permission, from Figure 1.4 from Pigliucci 121; p. 15) and Figure 1.1 from reference 38; p. 4)
Figure 3. Figure 3.

The panels above (A‐D) are representations of the general requirements needed for recognizing phenotypic plasticity as an adaptation. Although the representation of each of these criteria is pictorial simplistic, we acknowledge that their conclusive demonstration is quite complex. Accordingly, under each of the four general criteria we have listed additional considerations that should be taken into account. For extensive discussion of these criteria see text (also see reference 42. (A) Environmental heterogeneity must exist, and the degree of heterogeneity may determine the evolutionary (see panel D) consequences as opposed to an alteration in population mean. Heterogeneity may be biotic (e.g., predator presence) or abiotic (e.g., temperature), and special care should be taken to consider the extent of the spatial heterogeneity, the speed of fluctuations, and how these relate to the behavior and life history of the investigated organism. (B) Organisms must be able to reliably predict heterogeneity using environmental signals and these signals must be highly correlated with future environmental conditions. These signals must be spatial and temporally reliable and the organism must have the ability to sense and respond (even if the response is imperfect, see reference 87. (C) There must be an underlying genetic architecture regulating the plastic response. Here, we have presented methodologies for the evaluation of genetic variation. Using quantitative trait locus (QTL) mapping researchers may identify plasticity regions that directly affect the reaction norm (see text and Fig. 3 for examples), or evaluate differential gene expression in different environments with microarray technology. (D) There is a measurable response to selection and the response confers a fitness benefit.
Figure 4. Figure 4.

Conceptual representation of how one would assess whether plasticity is adaptive. Imagine environmentally induced variation in coloration, from light to dark, which is associated with seasonal, latitudinal, or elevation variation in temperature. One plastic (P) and three alternative nonplastic (N) genotypes expressing phenotypes in warm and cool environments are shown, with the three nonplastic genotypes expressing dark coloration, light coloration, and one of intermediate (i.e., moderate) coloration (Ndark, Nlight, and Nmoderate, respectively). The plastic genotype expresses light coloration when it is warm (e.g., summer phenotype), but dark coloration when it is cool (e.g., spring or autumn phenotypes). Suppose that when it is warm, a light‐colored phenotype avoids overheating. Thus, in the warm environment, the fitness of the plastic genotype (P) is similar to that of the nonplastic light‐colored genotype (Nlight), both of which are more fit than either of the other two nonplastic genotypes. Suppose also that in the cool environment, the plastic genotype (which now expresses a dark phenotype) has similar fitness to that of the nonplastic dark‐colored phenotype, because both can convert the absorbed solar radiation into higher body temperatures necessary for some components of fitness. Provided all of the previously mentioned conditions are true (and making certain assumptions about the probability of encountering both environments), we would conclude that the plasticity is adaptive, because there is no single nonplastic genotype that has, on average, higher fitness. However, if there is no cost to being dark under warm conditions, such that the fitness of the Ndark genotype is as high as that of the plastic genotype under the warm conditions, then there is no fitness advantage to being plastic. Therefore, we could not conclude that the plasticity is adaptive (and indeed, if there are costs to being plastic, per se, we might expect the plastic genotype to have lower fitness than the Ndark genotype, despite that both have the same beneficial, dark‐coloration phenotype under the cool conditions.)
Figure 5. Figure 5.

Depiction of the use of molecular techniques to better understand the genetic basis of phenotypic plasticity. Gene expression data were collected using a global transcriptome analysis of Fundulus heteroclitus gill tissue during hypo‐osmotic challenge. Log2 gene expression values are plotted as a function of time of exposure and partitioned by functional category. This figure is recreated, with permission, from 184 Whitehead et al. “Functional genomics of physiological plasticity and local adaptation in killifish”, Journal of Heredity, June 25, 2010, Figure 3, with permission of Oxford University Press on behalf of the American Genetic Association. Gene names are as follows: “14.3.3.a, 14‐3‐3.a protein; APOC1, apolipoprotein C‐I; APOM, apolipoprotein M; AQP‐3, aquaporin‐3; Arpc1a, actin‐related protein 2/3 complex subunit 1A; ATP6V1E1, V‐type proton ATPase subunit E 1; BCDO2, beta‐carotene dioxygenase 2; CALM, calmodulin; CFTR, cystic fibrosis transmembrane conductance regulator; CLD3, claudin‐3; CLD4, claudin‐4; CMC1, calcium‐binding mitochondrial carrier protein Aralar1; CP24A, 1,25‐dihydroxyvitamin D(3) 24‐hydroxylase; CX32, gap junction connexin‐32.2 protein; CYLC1, cylicin‐1; Dio1; DSC1, desmocollin‐1; ECH1, delta(3,5)‐delta(2,4)‐dienoyl‐CoA isomerase; ECHB, acetyl‐CoA acyltransferase; ERG28, probable ergosterol biosynthetic protein 28; F16PA, fructose‐1,6‐bisphosphatase class 1; G6PI, glucose‐6‐phosphate isomerase; GADD45, growth arrest and DNA damage‐inducible protein GADD45 beta; GSN, gelsolin; H1, histone H1; H2AX, histone H2A.x; H2B1, histone H2B.1; H4, histone H4; HMDH, 3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase; IMPA1, inositol monophosphatase; INO1, inositol‐3‐phosphate synthase; K6PF, 6‐phosphofructokinase; KCRB, creatine kinase B‐type; KCRM, creatine kinase M‐type; KCRT, creatine kinase, testis isozyme; KRT18, keratin, type I cytoskeletal 18; Marcks, myristoylated alanine‐rich C‐kinase substrate; MYL6, myosin light polypeptide 6; MYL7, myosin regulatory light chain 2, atrial isoform; NEFL, neurofilament light polypeptide; NKA, sodium/potassium‐transporting ATPase subunit alpha‐1; NKCC2, sodium/calcium exchanger 2; ODP2, pyruvate dehydrogenase complex E2 subunit; ODPB, pyruvate dehydrogenase E1 component subunit beta; Ostf1, Oreochromis mossambicus osmotic stress transcription factor 1; OTOP1, otopetrin‐1; PGM2, phosphoglucomutase‐2; PLCD1, phospholipase C‐delta‐1; PLEC1, plectin‐1; PYGM, glycogen phosphorylase; REDD‐1, DNA damage‐inducible transcript 4 protein; RHOA, transforming protein RhoA; S10AD, S100 calcium‐binding protein A13; SAPK‐3, MAP kinase p38γ; SCMC2, small calcium‐binding mitochondrial carrier protein 2; SIAS, sialic acid synthase; TBCA, tubulin‐specific chaperone A; Tnnt3, troponin T, fast skeletal muscle; TPI1, triosephosphate isomerase; TUB1, tubulin alpha chain” (184).
Figure 6. Figure 6.

An integrative approach to the study of phenotypic plasticity utilizing a mouse model. This figure is partially recreated, with permission, from 82 Kelly et al. “Genetic architecture of voluntary exercise in an advanced intercross line of mice” Physiological Genomics, 2010, 42: 190‐200, Figures 1 and 2; and 81 Kelly et al. “Exercise, weight loss, and changes in body composition in mice: phenotypic relationships and genetic architecture,” Physiological Genomics, 2011, 43: 199‐212, Figures 1 and 5. In this integrative example, we illustrate the complex control of a single phenotype, body mass, and the intricacies of its response to an altered environment, exercise. It has been well established that the variation in body mass is partially regulated by genetics (orange panel). Additionally, as demonstrated by the presence of an altered environment (exercise, as opposed to none) there is a plastic response in body weight (blue panel). Although this change is most commonly a reduction in weight, there is substantial variation among a given population (blue panel). Additionally, this variation in the change in body weight in response to exercise has a genetic basis as indicated by the presence of a quantitative trait locus (QTL) on chromosome 11 (red panel). Furthermore, the predisposition to engage in the altered environment (exercise or not) has an underlying genetic architecture (green panel). This type of comprehensive approach demonstrates the complexities of the study of phenotypic plasticity and the power of a molecular approach using a mouse model. For further discussion of examples from this mouse model see text and Garland and Kelly 56. Additionally, for a similar theoretical approach studying bone structure and performance see Middleton et al. 103. For the genome wide QTL plots, red traces are the simple mapping output, and black traces are corrected for family structure in this fourth generation population. The solid black and gray dotted lines represent the permuted 95% [logarithm of odds (LOD) ≥ 3.9, P ≤ 0.05] and 90% (LOD ≥ 3.5, P ≤ 0.1) LOD thresholds, respectively.


Figure 1.

Two genetically identical water fleas, Daphnia lumholtzi. The helmet and extended tail spine of the individual on the left were induced as a result of chemical cues from a predaceous fish and serve as protection 67. This figure is recreated, with permission, from 3 Agrawal, A. “Phenotypic plasticity in the interactions and evolution of species”, Science, October 12, 2001, 294:321‐326, Figure 1, with permission of D. Laforsch.



Figure 2.

Possible relationships among plasticity and genetic variation. In each panel, dots connected by lines represent the phenotypes expressed by each of three genotypes (or families), numbered 1, 2, and 3, in each of two alternative environments (A, B). These lines are the reaction norms. (A) The three genotypes differ in their phenotypes within each environment, indicating genetic variation (VG = yes). However, a given genotype expresses the same phenotype, regardless of environment; that is, the reaction norms are flat. Hence, there is no environmental effect on the phenotype (VE, or plasticity, is absent). Because the reactions norms are parallel, there is no genotype‐by‐environment interaction (GxE = no). (B) As in panel “A,” the three genotypes differ in phenotype within a given environment, but in addition, each genotype expresses a different phenotype in environment “B,” relative to that expressed in environment “A.” That is, the reaction norms are not flat; each genotype is plastic for the trait of interest. However, because the slopes of all reaction norms are parallel, there is no genotype‐by‐environment interaction. The genotypes, although plastic, are all similarly plastic (for the trait of interest). (C) Genotypes differ within environments, show plasticity, and differ in plasticity. That is, the reaction norms are not parallel, indicating genotype‐by‐environment interactions. In this case, reaction norms 2 and 3 cross, but this may not always be the case. This figure is conceptually similar to, and derived, with permission, from Figure 1.4 from Pigliucci 121; p. 15) and Figure 1.1 from reference 38; p. 4)



Figure 3.

The panels above (A‐D) are representations of the general requirements needed for recognizing phenotypic plasticity as an adaptation. Although the representation of each of these criteria is pictorial simplistic, we acknowledge that their conclusive demonstration is quite complex. Accordingly, under each of the four general criteria we have listed additional considerations that should be taken into account. For extensive discussion of these criteria see text (also see reference 42. (A) Environmental heterogeneity must exist, and the degree of heterogeneity may determine the evolutionary (see panel D) consequences as opposed to an alteration in population mean. Heterogeneity may be biotic (e.g., predator presence) or abiotic (e.g., temperature), and special care should be taken to consider the extent of the spatial heterogeneity, the speed of fluctuations, and how these relate to the behavior and life history of the investigated organism. (B) Organisms must be able to reliably predict heterogeneity using environmental signals and these signals must be highly correlated with future environmental conditions. These signals must be spatial and temporally reliable and the organism must have the ability to sense and respond (even if the response is imperfect, see reference 87. (C) There must be an underlying genetic architecture regulating the plastic response. Here, we have presented methodologies for the evaluation of genetic variation. Using quantitative trait locus (QTL) mapping researchers may identify plasticity regions that directly affect the reaction norm (see text and Fig. 3 for examples), or evaluate differential gene expression in different environments with microarray technology. (D) There is a measurable response to selection and the response confers a fitness benefit.



Figure 4.

Conceptual representation of how one would assess whether plasticity is adaptive. Imagine environmentally induced variation in coloration, from light to dark, which is associated with seasonal, latitudinal, or elevation variation in temperature. One plastic (P) and three alternative nonplastic (N) genotypes expressing phenotypes in warm and cool environments are shown, with the three nonplastic genotypes expressing dark coloration, light coloration, and one of intermediate (i.e., moderate) coloration (Ndark, Nlight, and Nmoderate, respectively). The plastic genotype expresses light coloration when it is warm (e.g., summer phenotype), but dark coloration when it is cool (e.g., spring or autumn phenotypes). Suppose that when it is warm, a light‐colored phenotype avoids overheating. Thus, in the warm environment, the fitness of the plastic genotype (P) is similar to that of the nonplastic light‐colored genotype (Nlight), both of which are more fit than either of the other two nonplastic genotypes. Suppose also that in the cool environment, the plastic genotype (which now expresses a dark phenotype) has similar fitness to that of the nonplastic dark‐colored phenotype, because both can convert the absorbed solar radiation into higher body temperatures necessary for some components of fitness. Provided all of the previously mentioned conditions are true (and making certain assumptions about the probability of encountering both environments), we would conclude that the plasticity is adaptive, because there is no single nonplastic genotype that has, on average, higher fitness. However, if there is no cost to being dark under warm conditions, such that the fitness of the Ndark genotype is as high as that of the plastic genotype under the warm conditions, then there is no fitness advantage to being plastic. Therefore, we could not conclude that the plasticity is adaptive (and indeed, if there are costs to being plastic, per se, we might expect the plastic genotype to have lower fitness than the Ndark genotype, despite that both have the same beneficial, dark‐coloration phenotype under the cool conditions.)



Figure 5.

Depiction of the use of molecular techniques to better understand the genetic basis of phenotypic plasticity. Gene expression data were collected using a global transcriptome analysis of Fundulus heteroclitus gill tissue during hypo‐osmotic challenge. Log2 gene expression values are plotted as a function of time of exposure and partitioned by functional category. This figure is recreated, with permission, from 184 Whitehead et al. “Functional genomics of physiological plasticity and local adaptation in killifish”, Journal of Heredity, June 25, 2010, Figure 3, with permission of Oxford University Press on behalf of the American Genetic Association. Gene names are as follows: “14.3.3.a, 14‐3‐3.a protein; APOC1, apolipoprotein C‐I; APOM, apolipoprotein M; AQP‐3, aquaporin‐3; Arpc1a, actin‐related protein 2/3 complex subunit 1A; ATP6V1E1, V‐type proton ATPase subunit E 1; BCDO2, beta‐carotene dioxygenase 2; CALM, calmodulin; CFTR, cystic fibrosis transmembrane conductance regulator; CLD3, claudin‐3; CLD4, claudin‐4; CMC1, calcium‐binding mitochondrial carrier protein Aralar1; CP24A, 1,25‐dihydroxyvitamin D(3) 24‐hydroxylase; CX32, gap junction connexin‐32.2 protein; CYLC1, cylicin‐1; Dio1; DSC1, desmocollin‐1; ECH1, delta(3,5)‐delta(2,4)‐dienoyl‐CoA isomerase; ECHB, acetyl‐CoA acyltransferase; ERG28, probable ergosterol biosynthetic protein 28; F16PA, fructose‐1,6‐bisphosphatase class 1; G6PI, glucose‐6‐phosphate isomerase; GADD45, growth arrest and DNA damage‐inducible protein GADD45 beta; GSN, gelsolin; H1, histone H1; H2AX, histone H2A.x; H2B1, histone H2B.1; H4, histone H4; HMDH, 3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase; IMPA1, inositol monophosphatase; INO1, inositol‐3‐phosphate synthase; K6PF, 6‐phosphofructokinase; KCRB, creatine kinase B‐type; KCRM, creatine kinase M‐type; KCRT, creatine kinase, testis isozyme; KRT18, keratin, type I cytoskeletal 18; Marcks, myristoylated alanine‐rich C‐kinase substrate; MYL6, myosin light polypeptide 6; MYL7, myosin regulatory light chain 2, atrial isoform; NEFL, neurofilament light polypeptide; NKA, sodium/potassium‐transporting ATPase subunit alpha‐1; NKCC2, sodium/calcium exchanger 2; ODP2, pyruvate dehydrogenase complex E2 subunit; ODPB, pyruvate dehydrogenase E1 component subunit beta; Ostf1, Oreochromis mossambicus osmotic stress transcription factor 1; OTOP1, otopetrin‐1; PGM2, phosphoglucomutase‐2; PLCD1, phospholipase C‐delta‐1; PLEC1, plectin‐1; PYGM, glycogen phosphorylase; REDD‐1, DNA damage‐inducible transcript 4 protein; RHOA, transforming protein RhoA; S10AD, S100 calcium‐binding protein A13; SAPK‐3, MAP kinase p38γ; SCMC2, small calcium‐binding mitochondrial carrier protein 2; SIAS, sialic acid synthase; TBCA, tubulin‐specific chaperone A; Tnnt3, troponin T, fast skeletal muscle; TPI1, triosephosphate isomerase; TUB1, tubulin alpha chain” (184).



Figure 6.

An integrative approach to the study of phenotypic plasticity utilizing a mouse model. This figure is partially recreated, with permission, from 82 Kelly et al. “Genetic architecture of voluntary exercise in an advanced intercross line of mice” Physiological Genomics, 2010, 42: 190‐200, Figures 1 and 2; and 81 Kelly et al. “Exercise, weight loss, and changes in body composition in mice: phenotypic relationships and genetic architecture,” Physiological Genomics, 2011, 43: 199‐212, Figures 1 and 5. In this integrative example, we illustrate the complex control of a single phenotype, body mass, and the intricacies of its response to an altered environment, exercise. It has been well established that the variation in body mass is partially regulated by genetics (orange panel). Additionally, as demonstrated by the presence of an altered environment (exercise, as opposed to none) there is a plastic response in body weight (blue panel). Although this change is most commonly a reduction in weight, there is substantial variation among a given population (blue panel). Additionally, this variation in the change in body weight in response to exercise has a genetic basis as indicated by the presence of a quantitative trait locus (QTL) on chromosome 11 (red panel). Furthermore, the predisposition to engage in the altered environment (exercise or not) has an underlying genetic architecture (green panel). This type of comprehensive approach demonstrates the complexities of the study of phenotypic plasticity and the power of a molecular approach using a mouse model. For further discussion of examples from this mouse model see text and Garland and Kelly 56. Additionally, for a similar theoretical approach studying bone structure and performance see Middleton et al. 103. For the genome wide QTL plots, red traces are the simple mapping output, and black traces are corrected for family structure in this fourth generation population. The solid black and gray dotted lines represent the permuted 95% [logarithm of odds (LOD) ≥ 3.9, P ≤ 0.05] and 90% (LOD ≥ 3.5, P ≤ 0.1) LOD thresholds, respectively.

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Article Title: Phenotypic Plasticity: Molecular Mechanisms and Adaptive Significance
 

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Scott A. Kelly, Tami M. Panhuis, Andrew M. Stoehr. Phenotypic Plasticity: Molecular Mechanisms and Adaptive Significance. Compr Physiol 2012, 2: 1417-1439. doi: 10.1002/cphy.c110008