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Evolutionary Physiology and Genomics in the Highly Adaptable Killifish (Fundulus heteroclitus)

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Abstract

By investigating evolutionary adaptations that change physiological functions, we can enhance our understanding of how organisms work, the importance of physiological traits, and the genes that influence these traits. This approach of investigating the evolution of physiological adaptation has been used with the teleost fish Fundulus heteroclitus and has produced insights into (i) how protein polymorphisms enhance swimming and development; (ii) the role of equilibrium enzymes in modulating metabolic flux; (iii) how variation in DNA sequences and mRNA expression patterns mitigate changes in temperature, pollution, and salinity; and (iv) the importance of nuclear‐mitochondrial genome interactions for energy metabolism. Fundulus heteroclitus provides so many examples of adaptive evolution because their local population sizes are large, they have significant standing genetic variation, and they experience large ranges of environmental conditions that enhance the likelihood that adaptive evolution will occur. Thus, F. heteroclitus research takes advantage of evolutionary changes associated with exposure to diverse environments, both across the North American Atlantic coast and within local habitats, to contrast neutral versus adaptive divergence. Based on evolutionary analyses contrasting neutral and adaptive evolution in F. heteroclitus populations, we conclude that adaptive evolution can occur readily and rapidly, at least in part because it depends on large amounts of standing genetic variation among many genes that can alter physiological traits. These observations of polygenic adaptation enhance our understanding of how evolution and physiological adaptation progresses, thus informing both biological and medical scientists about genotype‐phenotype relationships. © 2020 American Physiological Society. Compr Physiol 10:637‐671, 2020.

Figure 1. Figure 1. Demographic patterns among F. heteroclitus populations. (A) Neighbor‐joining tree based on microsatellites 6,60,200. (B) Maximum parsimony tree for RFLP in mitochondria, LDH‐B coding region, and 309 bp of Cytochrome B 19. Dark circles and squares are northern populations (north of Hudson River), unfilled are southern populations (south of Hudson River). (C) PCA and population structure (k = 9) based on 354 SNP, 30 individuals per population and principal component analysis 203,204.
Figure 2. Figure 2. Allozymes in F. heteroclitus. (A) Allelic variations of protein enzymes (allozymes) for three different genes: LDH‐B*, Malate dehydrogenase A (M, MDH‐A), and Isocitrate dehydrogenase‐B (I, IDH‐B) (redrawn based on data in Refs 148,159). The frequencies of the northern type alleles are plotted versus latitude along the eastern seacoast of North America (degrees latitude N). (B) Enzyme kinetics for the three genotypes of LDH‐B [catalytic turn‐over (kcat) divided by the Km] measured at different temperatures at neutral pH ([OH] = [H]) (redrawn based on data in Ref. 144). (C) Enzyme kinetics (kcat/Km) for the three LDH‐B genotypes plotted against temperature and pH (redrawn based on data in Ref. 144). (D) Hatching time at 20°C for fish from single populations for the three LDH‐B genotypes (redrawn based on data in Ref. 51). Hatching times were defined among 20 randomly crossed pairs, and larvae were genotyped. Data represent larvae genotypes. Similar results were obtained by 4 replicate mass crosses with 40 male and 40 female heterozygotes in each cross (n > 1000/cross). (E) Critical swimming speed (maximum sustainable swimming speeds in body lengths per second) for the two LDH‐B homozygotes; all fish were from the same population (drawn from data in Ref. 52).
Figure 3. Figure 3. Phylogenetic analyses of enzyme amounts. (A) Fundulus phylogeny 141. There are 15 taxa: 2 populations from 7 species and a single population from the outgroup. Boxes represent taxa with similar environmental temperature variation: Blue–geographic variation in temperature with northern taxa being colder. Red–lack of geographic variation in temperature. (B) Two phylogenetic methods for correcting for species similarities in enzyme amount among 15 Fundulus taxa versus naturally occurring mean annual environmental temperatures. Only the three enzymes were significantly related to environmental temperature after correcting for phylogeny: glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), pyruvate kinase (PYK), and LDH‐B. (C) F. heteroclitus glucose‐dependent metabolism versus multiple factor equation using three phylogenetically important enzymes (GAPDH, PYK, and LDH‐B). r2 = 0.866 (p < 0.005) 146.
Figure 4. Figure 4. Adaptive evolution of LDH‐B proximal promoter. (A) LDH‐B mRNA versus protein for northern (Maine) and southern (Georgia) individuals. When fish are acclimated to 20°C, increasing amounts of mRNA are associated with larger amounts of LDH‐B protein (measured as maximal enzyme activity (r2 = 0.81, p < 0.01, data from Ref. 168). (B) LDH‐B transcriptional binding sites and sequence variation. Functional sites are DNA sequences that bind protein transcriptional factors or affect transcription. (C) Individual promoter activity (line extending above columns are standard errors) defined by linking LDH‐B proximal promoter to luciferase reporter gene and transfected into rainbow trout liver cell line 46. Promoter activities from northern individuals are significantly greater than promoter activities from southern individuals (p < 0.001). (D) Promoter activity with different proximal promoter elements. Binding site 6fp (but not intervening sequence), and SP1 reduce expression, and without SP1, the northern promoter activity is no longer greater than southern promoter activity. (E) Evolutionary relationship using nonfunctional sequences among Fundulus species and within F. heteroclitus. (F) Evolutionary relationship using functional DNA sequences (affect promoter activity). Northern F. heteroclitus functional sequences are derived and significantly different from southern and F. grandis promoter DNA sequences. (G) Sliding window of DNA sequence variation within and between northern and southern F. heteroclitus. Data derived from Refs 46,169.
Figure 5. Figure 5. Microarray: genome‐wide patterns of mRNA expression. (A) Heat map of adaptively significant mRNA expression. Red and green colors are the relative low or high expression. Notice northern individuals share similar expression patterns and are significantly different from southern F. heteroclitus and F. grandis. (B) Volcano plot: log2 expression relative to mean expression for each mRNA versus statistical significance as −log10 p‐values. Gray box highlights the most significant mRNA where northern mRNA (blue circle) is statistically larger than both southern F. heteroclitus (red square) and F. grandis (green circle). Redrawn from Ref. 133.
Figure 6. Figure 6. Clinal adaptive variation in mRNA expression. (A) Five sample sites and their phylogenetic relationship microsatellite‐derived neighbor‐joining tree with median annual temperatures (°C) averaged over 30 years. Branching pattern is a neighbor‐joining tree constructed from pairwise Cavalli‐Sforza and Edwards' chord distances 33 calculated from microsatellite allele frequencies. (B) Relationships between phylogenetic and ecological effects on variation in gene expression. For each gene, the explained variation (r2) for phylogeny [genetic distance based on Cavalli‐Sforza and Edwards' chord distances 33 calculated from microsatellite allele frequencies] versus the explained variation (r2) for habitat temperatures. Venn diagram is for the numbers of genes that have significant regression with habitat temperature (orange), phylogeny (green), or both temperature and phylogeny (blue). Colors of spots in the graph correspond to Venn diagram. Enlarged spots are the 13 genes that regress significantly with habitat temperature after correcting for phylogeny (red circle; Venn diagram) using the phylogenetic generalized least squares (PGLS) approach, and thus appear to be evolving by natural selection. (C) Variation in mRNA expression within or among populations. Plotted are the log of variation. Ratio of variation is indicative of evolutionary processes (directional, stabilizing, balancing, or neutral). Redrawn from Ref. 200.
Figure 7. Figure 7. mRNA expression and cardiac metabolism. (A) Relative levels of cardiac metabolism for 16 individuals (8 per Maine and Georgia population). Cardiac metabolism was measured using glucose, fatty acid, and LKA (lactate, ketones, and ethanol) as substrates 136. Red is at least 1.75‐fold greater and green is at least 1.75‐fold lower than the overall mean. (B) Significant mRNA expression differences between individuals within a population (negative log10 values, thus 2 is equal to a p‐value of 1%) versus the fold difference (log2 values, thus 1 = twofold difference). Fold differences are relative to the overall mean for each mRNA. Green background shadowing shows mRNA with 1.5‐fold or less differences. p‐Values are truncated at values more than 10−17. (C) Patterns of mRNA expression among all 16 individuals (green is relatively low, red is relatively high). A subset of mRNAs coding for metabolic genes that show shared expression within groups that is significantly different among groups. (D) Fatty acid metabolic rates are relative to the mRNA expression. mRNA expression summarized as one of three primary biochemical pathways (two principal components each for glycolysis, TCA cycle, and oxidative phosphorylation). Similar patterns occur for glucose and LKA supported cardiac metabolism 136.
Figure 8. Figure 8. Local osmotic adaptation. F. heteroclitus population variation along a salinity gradient in the Chesapeake Bay. (A) Map of salinity gradient in the Chesapeake Bay, where experiments contrasted physiology and genomics of marine‐native (M), brackish‐native (BW), and freshwater‐native (FW) populations. (B) Plot of genetic similarity of individuals collected from the three Chesapeake populations, where neighboring populations were equally genetically distant from each other. (C) Principal component analysis of genes that are differentially expressed between populations but not affected by salinity challenge. Genes where the pattern of population divergence matches the neutral expectation [e.g. as established by pattern of genetic relatedness shown in (B)] are included in the left panel and genes where the patterns of population divergences consistent with adaptation in the freshwater population (blue) are included in the right panel. Pie chart shows the proportion of genes within this set that show the neutral or adaptive pattern. (D) Principal component analysis of genes that are differentially expressed between populations and that are differentially expressed during salinity challenge. Genes where the pattern of population divergence matches the neutral expectation [e.g. as established by the pattern of genetic relatedness shown in (B)] are included in the left panel and genes where the patterns of population divergence consistent with adaptation in the freshwater population (blue) are included in the right panel. Pie chart shows the proportion of genes within this set that show the neutral or adaptive pattern. A greater proportion of genes that are transcriptionally responsive to salinity show the adaptive pattern than genes that are not responsive to salinity. Principal component analyses are redrawn from Ref. 202. Salinity gradient heatmap of the Chesapeake Bay was generated from the NOAA Chesapeake Bay Operational Forecast System (https://tidesandcurrents.noaa.gov/).
Figure 9. Figure 9. Local adaptation to warmer temperatures. Three populations (triads) were examined: a northern and southern reference population and a locally heated thermal effluent (TE) population. (A) Genetic structure among Oyster Creek TE site using all approximately 5400 SNPs. X and Y axes are the first and second principal components (linear equation maximizing the variation among populations). The first principal component separates all three sites, and the second separates the TE site (red) from both northern and southern reference sites. (B) Outlier SNPs with statistically large and unexpected FST values for paired comparisons between TE and references. SNPs evolving by natural selection are the 94 SNPs where TE differs from both reference populations but are not different between the pair of reference populations. (C) Structure plots using 94 outlier SNPs for 2, 3, or 4 groups of individuals (k = 2, 3, or 4). TE site is distinct in all comparisons. (D) Linkage disequilibrium as indicated by similar FST values relative to the DNA distance (base pair, bp). Dashed line is the mean, and shading is the 95% confidence bounds for the mean genome‐wide FST value estimate for both TE versus reference comparisons. Red is the decline in FST value for outlier SNPs, and blue is the mean FST value when TE and references are randomly permutated.
Figure 10. Figure 10. Rapid local adaptation to pollution. Analyses of polluted populations using changes in allele frequencies for 354 SNPs defined by mass spectrophotometry. (A) In each of three comparisons a polluted population (P) was compared to two clean reference populations (C). The Venn diagram for these three triads (C‐P‐C) was used to identify statistically significant SNPs based on an outlier test (Outlier, unexpectedly large FST), environmental association of SNPs (Assoc.), and changes in minor allele frequencies (MAF). The red number is the number of SNPs that are significant in all three tests. (B) Maximum parsimony tree of the 24 CYP1A promoter‐intron sequences use to test the effect of DNA sequence variation on gene expression. Blue highlights are sequences from polluted New Bedford populations. Red stars are for sequences with derived outlier SNP. (C) Induction of gene expression with exposure to persistent organic pollutants (POP) in cells in culture for CYP1A promoter from polluted and clean populations. (D) Average pollution‐induced gene expression from CYP1A promoter from the two clean (green and blue) and the polluted New Bedford population (red). Letters represent post‐hoc analysis indicating that the polluted New Bedford is significantly different from both clean populations, and there is no significant difference between the clean populations.
Figure 11. Figure 11. Genomics of adaptation to recent anthropogenic pollution. (A) Four pairs of populations were sampled: for each pair, one population inhabits highly polluted marine environments and individuals are tolerant to POPs (T), and the second population is in a clean, nonpolluted reference site and individuals are sensitive to POPs (S). (B) Pairs of mRNA expression for controls and POP exposure among tolerant (T) and sensitive (S) populations. Each population has mRNA expression for two sets of conditions: control and exposure to POP. In each row is the relative expression of an mRNA, with high expression as bright yellow. The lower panel highlights genes activated by ligand‐bound AHR protein. (C) Diagram of AHR signaling pathway including co‐regulators and transcriptional targets. Color boxes are color coded for location defined in (A). Filled boxes are genes identified as evolving by natural selection. (D) FST values and π (pi, nucleotide diversity) between tolerant (T) and sensitive (S) populations. Gray shading highlights DNA sequences with unusually large significant FST values, extreme pi values, or both. These regions of the genome also have significant Tajima's D (not shown).
Figure 12. Figure 12. Epistatic adaptive evolution. (A) Oxidative Phosphorylation pathway and the number of protein subunits encoded by mitochondrial and nuclear genomes 95. (B) Mitochondrial OxPhos dependent respiration (State 3) measured in Fundulus heteroclitus from a single New Jersey population. Individuals were acclimated to either 12 or 28°C and had either the northern or southern mitochondrial haplotype. Acclimation, acute (assay temperature), and mitochondrial effects were all significant. (C) Distribution of wFST values for 11,705 nuclear SNPs calculated between the two mitochondrial haplotypes within the single population. Plot contains wFST values and corresponding negative log10 p‐values (e.g. −log10(0.01) = 2). Blue values are significant with a p‐value less than 0.01, green values are significant with a 1% FDR correction, and purple values are significant with a Bonferroni correction. Histograms show wFST and p‐value distributions. (D) Mitochondrial OxPhos dependent respiration (State 3) as a function of the fraction of southern nuclear alleles. State 3 is the residual from a mixed model with body mass, acclimation, and assay temperatures. Individuals with greater than 75% northern nuclear alleles and the northern mitochondria are blue, individuals with less than 75% northern nuclear alleles, and the northern mitochondria are green. Individuals with less than 75% southern nuclear alleles and with southern mitochondria are orange. Individuals with greater than 75% southern alleles and the southern mitochondria are red.
Figure 13. Figure 13. Fine‐scale evolution among microhabitats. (A) Three New Jersey saltmarsh estuaries (Mantoloking, Rutgers University Marine Field Station, Stone Harbor) and an enlarged image of Rutgers University Marine Field Station with three microhabitats Basin (B), Creek (C), and Pond (P). The distance between microhabitats was never greater than 200 m and usually less than 50 m. (B) Evolutionary analyses among microhabitats for three populations, where each population has three analyses: (1) SNPs with significantly different FST values, (2) Lositan identified significant outlier SNPs, and (3) Arlequin identified significant outlier SNPs. Significant SNPs detected in all three analyses with joint FDR less than 1% were considered outlier SNPs. (C) Density of FST values within each of the three New Jersey saltmarsh populations. Plotted are large significant outlier SNPs (blue), 4352 nonoutlier SNPs (gold), and SNPs when population assignment is randomly permuted among microhabitats (red). (D) Density of outlier‐SNP FST values within and among populations. Significant outlier SNP‐specific FST values for within Rutgers University Marine Field Station (blue) and between Rutgers Marine Station and Stone Harbor (gold) or Mantoloking (red).


Figure 1. Demographic patterns among F. heteroclitus populations. (A) Neighbor‐joining tree based on microsatellites 6,60,200. (B) Maximum parsimony tree for RFLP in mitochondria, LDH‐B coding region, and 309 bp of Cytochrome B 19. Dark circles and squares are northern populations (north of Hudson River), unfilled are southern populations (south of Hudson River). (C) PCA and population structure (k = 9) based on 354 SNP, 30 individuals per population and principal component analysis 203,204.


Figure 2. Allozymes in F. heteroclitus. (A) Allelic variations of protein enzymes (allozymes) for three different genes: LDH‐B*, Malate dehydrogenase A (M, MDH‐A), and Isocitrate dehydrogenase‐B (I, IDH‐B) (redrawn based on data in Refs 148,159). The frequencies of the northern type alleles are plotted versus latitude along the eastern seacoast of North America (degrees latitude N). (B) Enzyme kinetics for the three genotypes of LDH‐B [catalytic turn‐over (kcat) divided by the Km] measured at different temperatures at neutral pH ([OH] = [H]) (redrawn based on data in Ref. 144). (C) Enzyme kinetics (kcat/Km) for the three LDH‐B genotypes plotted against temperature and pH (redrawn based on data in Ref. 144). (D) Hatching time at 20°C for fish from single populations for the three LDH‐B genotypes (redrawn based on data in Ref. 51). Hatching times were defined among 20 randomly crossed pairs, and larvae were genotyped. Data represent larvae genotypes. Similar results were obtained by 4 replicate mass crosses with 40 male and 40 female heterozygotes in each cross (n > 1000/cross). (E) Critical swimming speed (maximum sustainable swimming speeds in body lengths per second) for the two LDH‐B homozygotes; all fish were from the same population (drawn from data in Ref. 52).


Figure 3. Phylogenetic analyses of enzyme amounts. (A) Fundulus phylogeny 141. There are 15 taxa: 2 populations from 7 species and a single population from the outgroup. Boxes represent taxa with similar environmental temperature variation: Blue–geographic variation in temperature with northern taxa being colder. Red–lack of geographic variation in temperature. (B) Two phylogenetic methods for correcting for species similarities in enzyme amount among 15 Fundulus taxa versus naturally occurring mean annual environmental temperatures. Only the three enzymes were significantly related to environmental temperature after correcting for phylogeny: glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), pyruvate kinase (PYK), and LDH‐B. (C) F. heteroclitus glucose‐dependent metabolism versus multiple factor equation using three phylogenetically important enzymes (GAPDH, PYK, and LDH‐B). r2 = 0.866 (p < 0.005) 146.


Figure 4. Adaptive evolution of LDH‐B proximal promoter. (A) LDH‐B mRNA versus protein for northern (Maine) and southern (Georgia) individuals. When fish are acclimated to 20°C, increasing amounts of mRNA are associated with larger amounts of LDH‐B protein (measured as maximal enzyme activity (r2 = 0.81, p < 0.01, data from Ref. 168). (B) LDH‐B transcriptional binding sites and sequence variation. Functional sites are DNA sequences that bind protein transcriptional factors or affect transcription. (C) Individual promoter activity (line extending above columns are standard errors) defined by linking LDH‐B proximal promoter to luciferase reporter gene and transfected into rainbow trout liver cell line 46. Promoter activities from northern individuals are significantly greater than promoter activities from southern individuals (p < 0.001). (D) Promoter activity with different proximal promoter elements. Binding site 6fp (but not intervening sequence), and SP1 reduce expression, and without SP1, the northern promoter activity is no longer greater than southern promoter activity. (E) Evolutionary relationship using nonfunctional sequences among Fundulus species and within F. heteroclitus. (F) Evolutionary relationship using functional DNA sequences (affect promoter activity). Northern F. heteroclitus functional sequences are derived and significantly different from southern and F. grandis promoter DNA sequences. (G) Sliding window of DNA sequence variation within and between northern and southern F. heteroclitus. Data derived from Refs 46,169.


Figure 5. Microarray: genome‐wide patterns of mRNA expression. (A) Heat map of adaptively significant mRNA expression. Red and green colors are the relative low or high expression. Notice northern individuals share similar expression patterns and are significantly different from southern F. heteroclitus and F. grandis. (B) Volcano plot: log2 expression relative to mean expression for each mRNA versus statistical significance as −log10 p‐values. Gray box highlights the most significant mRNA where northern mRNA (blue circle) is statistically larger than both southern F. heteroclitus (red square) and F. grandis (green circle). Redrawn from Ref. 133.


Figure 6. Clinal adaptive variation in mRNA expression. (A) Five sample sites and their phylogenetic relationship microsatellite‐derived neighbor‐joining tree with median annual temperatures (°C) averaged over 30 years. Branching pattern is a neighbor‐joining tree constructed from pairwise Cavalli‐Sforza and Edwards' chord distances 33 calculated from microsatellite allele frequencies. (B) Relationships between phylogenetic and ecological effects on variation in gene expression. For each gene, the explained variation (r2) for phylogeny [genetic distance based on Cavalli‐Sforza and Edwards' chord distances 33 calculated from microsatellite allele frequencies] versus the explained variation (r2) for habitat temperatures. Venn diagram is for the numbers of genes that have significant regression with habitat temperature (orange), phylogeny (green), or both temperature and phylogeny (blue). Colors of spots in the graph correspond to Venn diagram. Enlarged spots are the 13 genes that regress significantly with habitat temperature after correcting for phylogeny (red circle; Venn diagram) using the phylogenetic generalized least squares (PGLS) approach, and thus appear to be evolving by natural selection. (C) Variation in mRNA expression within or among populations. Plotted are the log of variation. Ratio of variation is indicative of evolutionary processes (directional, stabilizing, balancing, or neutral). Redrawn from Ref. 200.


Figure 7. mRNA expression and cardiac metabolism. (A) Relative levels of cardiac metabolism for 16 individuals (8 per Maine and Georgia population). Cardiac metabolism was measured using glucose, fatty acid, and LKA (lactate, ketones, and ethanol) as substrates 136. Red is at least 1.75‐fold greater and green is at least 1.75‐fold lower than the overall mean. (B) Significant mRNA expression differences between individuals within a population (negative log10 values, thus 2 is equal to a p‐value of 1%) versus the fold difference (log2 values, thus 1 = twofold difference). Fold differences are relative to the overall mean for each mRNA. Green background shadowing shows mRNA with 1.5‐fold or less differences. p‐Values are truncated at values more than 10−17. (C) Patterns of mRNA expression among all 16 individuals (green is relatively low, red is relatively high). A subset of mRNAs coding for metabolic genes that show shared expression within groups that is significantly different among groups. (D) Fatty acid metabolic rates are relative to the mRNA expression. mRNA expression summarized as one of three primary biochemical pathways (two principal components each for glycolysis, TCA cycle, and oxidative phosphorylation). Similar patterns occur for glucose and LKA supported cardiac metabolism 136.


Figure 8. Local osmotic adaptation. F. heteroclitus population variation along a salinity gradient in the Chesapeake Bay. (A) Map of salinity gradient in the Chesapeake Bay, where experiments contrasted physiology and genomics of marine‐native (M), brackish‐native (BW), and freshwater‐native (FW) populations. (B) Plot of genetic similarity of individuals collected from the three Chesapeake populations, where neighboring populations were equally genetically distant from each other. (C) Principal component analysis of genes that are differentially expressed between populations but not affected by salinity challenge. Genes where the pattern of population divergence matches the neutral expectation [e.g. as established by pattern of genetic relatedness shown in (B)] are included in the left panel and genes where the patterns of population divergences consistent with adaptation in the freshwater population (blue) are included in the right panel. Pie chart shows the proportion of genes within this set that show the neutral or adaptive pattern. (D) Principal component analysis of genes that are differentially expressed between populations and that are differentially expressed during salinity challenge. Genes where the pattern of population divergence matches the neutral expectation [e.g. as established by the pattern of genetic relatedness shown in (B)] are included in the left panel and genes where the patterns of population divergence consistent with adaptation in the freshwater population (blue) are included in the right panel. Pie chart shows the proportion of genes within this set that show the neutral or adaptive pattern. A greater proportion of genes that are transcriptionally responsive to salinity show the adaptive pattern than genes that are not responsive to salinity. Principal component analyses are redrawn from Ref. 202. Salinity gradient heatmap of the Chesapeake Bay was generated from the NOAA Chesapeake Bay Operational Forecast System (https://tidesandcurrents.noaa.gov/).


Figure 9. Local adaptation to warmer temperatures. Three populations (triads) were examined: a northern and southern reference population and a locally heated thermal effluent (TE) population. (A) Genetic structure among Oyster Creek TE site using all approximately 5400 SNPs. X and Y axes are the first and second principal components (linear equation maximizing the variation among populations). The first principal component separates all three sites, and the second separates the TE site (red) from both northern and southern reference sites. (B) Outlier SNPs with statistically large and unexpected FST values for paired comparisons between TE and references. SNPs evolving by natural selection are the 94 SNPs where TE differs from both reference populations but are not different between the pair of reference populations. (C) Structure plots using 94 outlier SNPs for 2, 3, or 4 groups of individuals (k = 2, 3, or 4). TE site is distinct in all comparisons. (D) Linkage disequilibrium as indicated by similar FST values relative to the DNA distance (base pair, bp). Dashed line is the mean, and shading is the 95% confidence bounds for the mean genome‐wide FST value estimate for both TE versus reference comparisons. Red is the decline in FST value for outlier SNPs, and blue is the mean FST value when TE and references are randomly permutated.


Figure 10. Rapid local adaptation to pollution. Analyses of polluted populations using changes in allele frequencies for 354 SNPs defined by mass spectrophotometry. (A) In each of three comparisons a polluted population (P) was compared to two clean reference populations (C). The Venn diagram for these three triads (C‐P‐C) was used to identify statistically significant SNPs based on an outlier test (Outlier, unexpectedly large FST), environmental association of SNPs (Assoc.), and changes in minor allele frequencies (MAF). The red number is the number of SNPs that are significant in all three tests. (B) Maximum parsimony tree of the 24 CYP1A promoter‐intron sequences use to test the effect of DNA sequence variation on gene expression. Blue highlights are sequences from polluted New Bedford populations. Red stars are for sequences with derived outlier SNP. (C) Induction of gene expression with exposure to persistent organic pollutants (POP) in cells in culture for CYP1A promoter from polluted and clean populations. (D) Average pollution‐induced gene expression from CYP1A promoter from the two clean (green and blue) and the polluted New Bedford population (red). Letters represent post‐hoc analysis indicating that the polluted New Bedford is significantly different from both clean populations, and there is no significant difference between the clean populations.


Figure 11. Genomics of adaptation to recent anthropogenic pollution. (A) Four pairs of populations were sampled: for each pair, one population inhabits highly polluted marine environments and individuals are tolerant to POPs (T), and the second population is in a clean, nonpolluted reference site and individuals are sensitive to POPs (S). (B) Pairs of mRNA expression for controls and POP exposure among tolerant (T) and sensitive (S) populations. Each population has mRNA expression for two sets of conditions: control and exposure to POP. In each row is the relative expression of an mRNA, with high expression as bright yellow. The lower panel highlights genes activated by ligand‐bound AHR protein. (C) Diagram of AHR signaling pathway including co‐regulators and transcriptional targets. Color boxes are color coded for location defined in (A). Filled boxes are genes identified as evolving by natural selection. (D) FST values and π (pi, nucleotide diversity) between tolerant (T) and sensitive (S) populations. Gray shading highlights DNA sequences with unusually large significant FST values, extreme pi values, or both. These regions of the genome also have significant Tajima's D (not shown).


Figure 12. Epistatic adaptive evolution. (A) Oxidative Phosphorylation pathway and the number of protein subunits encoded by mitochondrial and nuclear genomes 95. (B) Mitochondrial OxPhos dependent respiration (State 3) measured in Fundulus heteroclitus from a single New Jersey population. Individuals were acclimated to either 12 or 28°C and had either the northern or southern mitochondrial haplotype. Acclimation, acute (assay temperature), and mitochondrial effects were all significant. (C) Distribution of wFST values for 11,705 nuclear SNPs calculated between the two mitochondrial haplotypes within the single population. Plot contains wFST values and corresponding negative log10 p‐values (e.g. −log10(0.01) = 2). Blue values are significant with a p‐value less than 0.01, green values are significant with a 1% FDR correction, and purple values are significant with a Bonferroni correction. Histograms show wFST and p‐value distributions. (D) Mitochondrial OxPhos dependent respiration (State 3) as a function of the fraction of southern nuclear alleles. State 3 is the residual from a mixed model with body mass, acclimation, and assay temperatures. Individuals with greater than 75% northern nuclear alleles and the northern mitochondria are blue, individuals with less than 75% northern nuclear alleles, and the northern mitochondria are green. Individuals with less than 75% southern nuclear alleles and with southern mitochondria are orange. Individuals with greater than 75% southern alleles and the southern mitochondria are red.


Figure 13. Fine‐scale evolution among microhabitats. (A) Three New Jersey saltmarsh estuaries (Mantoloking, Rutgers University Marine Field Station, Stone Harbor) and an enlarged image of Rutgers University Marine Field Station with three microhabitats Basin (B), Creek (C), and Pond (P). The distance between microhabitats was never greater than 200 m and usually less than 50 m. (B) Evolutionary analyses among microhabitats for three populations, where each population has three analyses: (1) SNPs with significantly different FST values, (2) Lositan identified significant outlier SNPs, and (3) Arlequin identified significant outlier SNPs. Significant SNPs detected in all three analyses with joint FDR less than 1% were considered outlier SNPs. (C) Density of FST values within each of the three New Jersey saltmarsh populations. Plotted are large significant outlier SNPs (blue), 4352 nonoutlier SNPs (gold), and SNPs when population assignment is randomly permuted among microhabitats (red). (D) Density of outlier‐SNP FST values within and among populations. Significant outlier SNP‐specific FST values for within Rutgers University Marine Field Station (blue) and between Rutgers Marine Station and Stone Harbor (gold) or Mantoloking (red).
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Teaching Material

Douglas L. Crawford, Patricia M. Schulte, Andrew Whitehead, and Marjorie F. Oleksiak. Evolutionary Physiology and Genomics in the Highly Adaptable Killifish (Fundulus heteroclitus). Compr Physiol 10 : 2020, 637-671.

Didactic Synopsis

Major Teaching Points:

Evolutionary studies in the small teleost fish Fundulus heteroclitus have revealed how genetic variation influences biochemical and physiological function.

o These findings include:

o Genetic variation between LDH-B alleles that alters swimming abilities and development.

o DNA sequence variation that changes LDH-B enzyme expression.

o Quantitative differences in glycolytic enzyme expression related to thermal environment and how they modify cardiac metabolism.

o mRNA expression changes that are influenced by physiological acclimation and alter metabolism.

o Population-specific variation in DNA sequences and mRNA expression that mitigates variation in temperature, pollution, and osmotic environments.

o DNA sequence variation within and between nuclear and mitochondrial genomes and their interactions that alter mitochondrial metabolism.

o Based on research contrasting neutral and adaptive hypotheses, we suggest adaptive evolution in Fundulus is common because local populations have significant standing genetic variation and adaptive evolution typically proceeds by polygenic selection.

o If evolutionary adaptation often proceeds by polygenic selection involving small changes in allele frequencies in many genes, as is the case in Fundulus, then results from this system can enhance our general understanding of biochemical, physiological and evolutionary processes.

Didactic Legends

The following legends to the figures that appear throughout the article are written to be useful for teaching.

Figure 1. Teaching Points: Populations of F. heteroclitus along the eastern North Atlantic seacoast are more closely related on either side of the Hudson River. This pattern is seen in 1A with microsatellites (neutral markers) where Maine (ME) and Connecticut (CT) cluster separately from New Jersey (NJ); even though NJ and CT are geographically closer. Other molecular markers provide similar data. These include LDH-B sequences, mitochondrial DNA and 354 variable sites in the nuclear genome.

Figure 2. Teaching Points: A: Along the eastern seacoast of North America, F. heteroclitus has a clinal variation in the genetic variants (alleles) for many different enzymes. Three of these patterns are shown here: MDH-A with a steep change of allele frequencies at the Hudson River, IDH-B, with a small change in allele frequencies and LDH-B with a gradual change in allele frequencies. B &C: LDH-B enzyme reaction rates (kinetic constants) with different genotypes (homozygotes for the northern allele type, heterozygotes and homozygotes for southern allele type). These different LDH-B genotypes affect both hatching times (D) and swimming speeds (E). For the enzyme kinetics, hatching and swimming speeds, all fish are from the same population, ensuring a random genetic background, thus differences can be attributed to LDH-B genotypes.

Figure 3. Teaching Points: A. The evolutionary relationships among 15 populations and species of Fundulus. Blue boxes have large differences in natural environmental temperature between populations. B. Phylogenetically corrected enzyme amounts for the three enzymes that are related to environmental temperatures. Phylogenetic correction is analogous to correcting for body mass and provides measures of enzymes as a phylogenetically independent value for each population or species. C. Metabolic rates for northern and southern F. heteroclitus populations are a function of the amount of three enzymes GAPDH- glyceraldehyde-3-phosphate dehydrogenase; PYK pyruvate kinase and LDH-B.

Figure 4. Teaching Points: A. LDH-B mRNA expression defines the amount of LDH-B enzyme, and the variation in mRNA expression is related to the LDH-B proximal promoter. (B) DNA sequence variation where there is surprisingly more variation in functional sites that bind transcription factors than in non-functional sites. C. LDH-B proximal promoters from northern and southern individuals demonstrate that the DNA sequence variation between F. heteroclitus populations effect a difference in mRNA expression. D. Deleting functional DNA but not non-functional sites affects promoter activity and mRNA expression. E and F. Evolutionary relationships within and between species demonstrating that non-functional DNA sequence are similar to neutral expectation, but the functional sites demonstrate much greater difference between populations than between species. This pattern is indicative of evolution by natural selection. G. Analysis of DNA sequence variation indicating non-neutral patterns for functional DNA sequences.

Figure 5. Teaching Points: Genome wide patterns of mRNA expression measured with microarrays. A. Relative expression of adaptively important genes showing that northern individuals have different expression than both southern and F. grandis individuals. B. Plot of relative expression level (as log2 values, thus 1.0 = 2 fold difference) and probability of being significantly different (as negative log10 values, thus 2 = 0.01 for northern versus both southern and F. grandis. Gray box highlights p-value ~0.0001, with less than 1.5 fold difference.

Figure 6. Teaching Points:. mRNA expression was measured among five F. heteroclitus populations distributed along the eastern seacoast of North America (A). Because demography and thus neutral evolutionary processes can create difference among these five populations, the effect of demography was compared (Y-axis) to the effect of habitat temperature (B). The enlarged spots are adaptively important because there is a significant relationship between mRNA expression and habitat after removing demographic effects. (C) Patterns of variation within and between populations are indicative of different evolutionary effects.

Figure 7. Teaching Points: A. mRNA expression predicts heart metabolic rates when supported by glucose, fatty-acid or mixture of secondary metabolites (lactate, ketones and ethyl alcohol). Individuals differ in their overall metabolism and which substrate supports the highest metabolic rates. B. Significant difference in mRNA expression among individuals within each population (Maine and Georgia). Most mRNAs have a significant difference between individuals (p < 0.01 to 10-17) even though the magnitude of the difference (fold-change) is relatively small (few genes have 2-fold or more differences among individuals). C. Three groups of individuals that share similar patterns of expression such that individuals in one group are up (red in group 3) and individuals in another group are down (green group 2). D. mRNA expression was summarized as a linear combination of genes in three primary biochemical pathways: glycolysis, TCA-cycle and oxidative phosphorylation. The mRNA from different biochemical pathway predict fatty-acid metabolic rates for different groups of individuals. For example, TCA mRNAs explain 64% of the variation in fatty-acid dependent cardiac metabolism for group 1, while oxidative phosphorylation mRNAs or glycolytic mRNAs explain group 2 and 3 cardiac metabolism (respectively).

Figure 8. Teaching Points:.F. heteroclitus populations live in marine environments with different salinities that are a few hundred kilometers apart (A). These populations differ genetically due to both neutral and potentially adaptive genetic variation (B). The change in mRNA expression in response to different salinities depends on whether the genes evolve by neutral or adaptive processes.

Figure 9. Teaching Points: With a triads (3 populations: one hot TE and two cool reference populations north and south of the TE), (A) we can distinguish between populations, and more importantly, TE is different from both references. We can identify the SNPs that have the greatest effect on genetic distance (FST) as "outlier SNPs": SNPs with statistically unexpectedly large FST values. For this study (B), the 94 "adaptive SNPs" or SNPs evolving by natural selection have statistically large FST values between the TE and both references but are not different between references. These 94 SNPs reveal population structure where the TE population is unique and different from both references (C). Surprisingly, there is little linkage among SNPs (D). That is, the FST values for outlier SNPs declines to random expectation after 25 bp of DNA.

Figure 10. Teaching Points: A. illustrates the three locations where each location has a polluted and two clean reference populations (triad). Each of these locations was used to define changes in DNA sequences for 354 separate SNPs. The Venn diagram illustrated the number of SNPs with DNA changes shared for three different statistical tests. The red number is the number of SNPs that are statistically significant for all three tests. B and C, show how DNA promoter sequences effect a change in gene expression when exposed to pollution. Each promoter was linked to a reporter gene and cultured cells were transformed with these DNA sequences. Thus, in the same genetic cell line the promoter sequences from polluted populations induce higher gene expression than promoter sequences from clean reference populations. In C, populations with different letters are statistically different.

Figure 11. Teaching Points: A. Shows the pairs of populations used in this study where one population inhabiting polluted water was compared with a population inhabiting clean water. One of the comparisons (B) was mRNA expression patterns revealing that sensitive populations, but not tolerant populations, responded to pollution exposure, and this was particularly relevant to AHR regulated genes (lower panel in B). The diagram (C) illustrates AHR regulatory pathway with filled boxes indicating genes evolving by natural selection. These change in mRNA expression, especially associated with the AHR pathway, have patterns of DNA sequence variation indicative of evolution by natural selection (D).

Figure 12. Teaching Points: A. Mitochondrial metabolism produces most of the cells ATP via the Oxidative Phosphorylation pathway. This pathway has five enzyme complexes with a total 91 proteins – 13 from the mitochondrial genome and 78 from the nuclear genome. B. In a single population the mitochondrial respiration is affected by acclimation, assay temperature (acute effects), and an individual's mitochondria. C. Within this population are 349 nuclear DNA sequence variants (SNPs) that have large and statistically unlikely differences in allele frequencies between mitochondrial haplotypes. This difference in nuclear allele frequencies is denoted as wFST value—FST values within a population between mitochondrial types. D. The difference in nuclear SNPs affect mitochondrial respiration: individuals with more "southern" nuclear alleles, regardless of which mitochondria have higher metabolic rates.

 

Figure 13. Teaching Points: Three saltmarsh estuaries were examined along the New Jersey shore. In each saltmarsh there are permanent ponds and intertidal creeks that drain into basins (A). To determine if there was significant, potentially adaptive divergence, three statistical tests were applied to SNP allele frequencies. SNPs that were significant for all three tests were considered outlier SNPs that are most likely evolving by natural selection (B). The FST values for outlier, non-outlier and randomized data sets is shown for each population (C) and compared between Rutgers NJ and either Stone Harbor, NJ or Mantoloking, NJ.


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How to Cite

Douglas L. Crawford, Patricia M. Schulte, Andrew Whitehead, Marjorie F. Oleksiak. Evolutionary Physiology and Genomics in the Highly Adaptable Killifish (Fundulus heteroclitus). Compr Physiol 2020, 10: 637-671. doi: 10.1002/cphy.c190004