Genetic and phenotypic variation in sockeye salmon
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Genetic and phenotypic variation in sockeye salmon

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My Master's defense recapping the SNP assessment and sockeye senescence projects I worked on during my tenure as a grad student at the University of Washington.

My Master's defense recapping the SNP assessment and sockeye senescence projects I worked on during my tenure as a grad student at the University of Washington.

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  • Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
  • Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
  • Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
  • Mention allele
  • Nucleotide represents an allele
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components. BELS and WHICHLOCI provide each locus a rank based on the accuracy of individual assignment for that locus and the value lost when the locus is removed from the panel in a jackknife fashion. Loci that result in the greatest loss in individual assignment performance when removed receive the highest score.
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components. BELS and WHICHLOCI provide each locus a rank based on the accuracy of individual assignment for that locus and the value lost when the locus is removed from the panel in a jackknife fashion. Loci that result in the greatest loss in individual assignment performance when removed receive the highest score.
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
  • FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
  • Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
  • Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
  • Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
  • Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
  • FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
  • FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
  • FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
  • FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
  • FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
  • The probability of correct assignment from ONCOR is the probability that individual of unknown origin belongs to a given population in the baseline. A very simplified explanation of how the probability is calculated is that it uses the frequency of an unknown fish's genotype in a baseline pop and estimated mixture proportions (which is a conditional maximum likelihood estimate).
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • BELS is the poorest performing, in fact it is the only 96-SNP panel that does not perform better than all 48-SNP panels
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
  • demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
  • demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
  • demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
  • demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
  • decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
  • decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
  • decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
  • decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
  • decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
  • public sequence archives from Genebank.
  • public sequence archives from Genebank.
  • olfactory receptor neurons (ORN) to transmit signals of specific odors of amino acids and bile salts to their brain that sockeye salmon displaced from their spawning grounds within Hansen Creek returned to their breeding site
  • olfactory receptor neurons (ORN) to transmit signals of specific odors of amino acids and bile salts to their brain that sockeye salmon displaced from their spawning grounds within Hansen Creek returned to their breeding site

Genetic and phenotypic variation in sockeye salmon Genetic and phenotypic variation in sockeye salmon Presentation Transcript

  • Genetic and phenotypic diversity insockeye salmon, Oncorhynchus nerka Caroline Storer University of Washington School of Aquatic and Fishery Sciences Committee: Thomas Quinn Steven Roberts (Co-chair) James Seeb (Co-chair) William Templin 1
  • Outline• Introduction – Sockeye salmon• Chapter 1: – Evaluating the performance of SNPs for individual assignment• Chapter 2: – Characterizing differences in gene expression patterns associated with variability in senescence 2
  • 3
  • 4
  • 5
  • 6
  • 7
  • Sockeye Salmon• Anadromous• Natal homing• Undergo rapid senescence• Semelparous 8
  • Motivations• Improved fisheries management – Developing new management tools 9
  • 10
  • Fisheries Management• Applying genetics to fisheries management 11
  • Fisheries Management• Applying genetics to fisheries management- Population structure- Inferring population history- Parentage analysis- Fisheries forensics- Estimating mixed stock composition 12
  • Fisheries Management • Applying genetics to fisheries management Russia Alaska Bering Sea 13Habicht et al. 2010
  • Molecular Markers, Today• Single nucleotide polymorphisms (SNPs) 14
  • Molecular Markers, Today• Single nucleotide polymorphisms (SNPs) ACTCG ACACG SNP locus 15
  • Molecular Markers, Today• Single nucleotide polymorphisms (SNPs) - Abundant ACTCG - The number of available markers is growing - Methods are robust and ACACG automated - Not all SNPs are equal SNP locus 16
  • Chapter 1: Objectives• Develop new SNP markers for sockeye salmon 17
  • Chapter 1: Objectives• Develop new SNP markers for sockeye salmon• Rank all SNPs in sockeye salmon based on performance 18
  • Chapter 1: Objectives• Develop new SNP markers for sockeye salmon• Rank all SNPs in sockeye salmon based on performance• Evaluate the success of different ranking methods 19
  • Measuring Genetic Variation South-central Bristol Bay AlaskaRussia British Alaska Peninsula Columbia WashingtonGenotyped 12 populations, 61- 93 fish per population, using 114 SNPs 20
  • Measuring Genetic Variation Bristol Bay AlaskaPrincipal Coordinate 2 (15.5%) Peninsula Washington South-central Alaska British Columbia Russia Principal Coordinate 1 (44.5%) 21
  • Bristol Bay Alaska Peninsula South-central AlaskaRussia British Columbia Washington 22
  • SNP Ranking• Performed using only half of available individuals – Remaining individuals reserved for panel testing 23
  • SNP Ranking• Performed using only half of available individuals – Remaining individuals reserved for panel testing• Each SNP ranked by 5 measures 24
  • SNP Ranking• FST - SNPs ranked by ability to measure population variance 25
  • SNP Ranking• FST - SNPs ranked by ability to measure population variance• Informativness (In) - Potential for a genotype to belong to specific population versus a population average 26
  • SNP Ranking• FST - SNPs ranked by ability to measure population variance• Informativness (In) - Potential for a genotype to belong to specific population versus a population average• Locus contribution (LC) - Average contribution of each SNP to principal components 27
  • SNP Ranking• FST - SNPs ranked by ability to measure population variance• Informativness (In) - Potential for a genotype to belong to specific population versus a population average• Locus contribution (LC) - Average contribution of each SNP to principal components• BELS - SNPs ranked by reduction in performance when removed 28
  • SNP Ranking• FST - SNPs ranked by ability to measure population variance• Informativness (In) - Potential for a genotype to belong to specific population versus a population average• Locus contribution (LC) - Average contribution of each SNP to principal components• BELS - SNPs ranked by reduction in performance when removed• WHICHLOCI - Algorithm for ranking SNPs based on power for individual assignment 29
  • SNP Ranking 1 21Average SNP rank 41 61 81 top ranked SNPs 101 0 10 20 30 40 50 60 70 80 90 100 110 30 SNPs ordered by average rank
  • Panel Design• Created 48- and 96-SNP panels containing top ranked SNPs – for each of the five ranking measures – for average SNP rank – for randomly selected SNPs 31
  • Panel Design96-SNP Panels 32
  • Panel Design96-SNP Panels 33
  • Panel Design96-SNP Panels 34
  • Panel Design96-SNP Panels 35
  • Panel Design48-SNP Panels 36
  • Panel Design48-SNP Panels 37
  • Panel Design48-SNP Panels 38
  • Panel Design48-SNP Panels 39
  • Panel Design48-SNP Panels 40
  • Panel Testing• 2 panel testing methods 41
  • Panel Testing• 2 panel testing methods – Empirical • Remaining individuals assigned to a baseline of individuals used for SNP ranking • Assignment tests performed in ONCOR 42
  • Panel Testing• 2 panel testing methods – Empirical • Remaining individuals assigned to a baseline of individuals used for SNP ranking • Assignment tests performed in ONCOR – Simulated • 1000 individuals simulated using population allele frequencies from remaining individuals • Assignment tests replicated 500 times 43
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 44
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 45
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 46
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 47
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 48
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 49
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 50
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 51
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 52
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 53
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 54
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 55
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 56
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 57
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 58
  • Panel Testing – Empirical data 1.0Probability of correct assignment 0.8 0.6 0.4 0.2 0.0 59
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 60
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 61
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 62
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 63
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 64
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 65
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 66
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 67
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 68
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 69
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 70
  • Panel Testing – Simulated data 1.0Probability of correct assignment 0.9 0.8 0.7 71
  • Findings• Greater variation and lower panel performance using empirical data 72
  • Findings• Greater variation and lower panel performance using empirical data• In general, 96-SNP panels performed better 73
  • Findings• Greater variation and lower panel performance using empirical data• In general, 96-SNP panels performed better• FST, In, and LC panels had the highest average probability of correct assignment 74
  • Findings• Greater variation and lower panel performance using empirical data• In general, 96-SNP panels performed better• FST, In, and LC panels had the highest average probability of correct assignment• Random SNP selection preforms nearly as well as ranking when all available SNPs are used 75
  • Findings• Greater variation and lower panel performance using empirical data• In general, 96-SNP panels performed better• FST, In, and LC panels had the highest average probability of correct assignment• Random SNP selection preforms nearly as well as ranking when all available SNPs are used• BELS panels had the lowest average probability of correct assignment 76
  • Conclusions• Common ranking methods perform differently 77
  • Conclusions• Common ranking methods perform differently• More SNPs is often better 78
  • Conclusions• Common ranking methods perform differently• More SNPs is often better• When choosing a small proportion of available SNPs the ranking approach is more important 79
  • Conclusions• Common ranking methods perform differently• More SNPs is often better• When choosing a small proportion of available SNPs the ranking approach is more important• Empirical panel tests performance on real (vs. simulated) populations 80
  • Conclusions• Common ranking methods perform differently• More SNPs is often better• When choosing a small proportion of available SNPs the ranking approach is more important• Empirical panel tests performance on real (vs. simulated) populations• Simulated data highlights performance based on SNP composition 81
  • Implications• 43 new SNPs are now available for sockeye salmon 82
  • Implications• 43 new SNPs are now available for sockeye salmon - Already in use 40 Stock Togiak Catch (millions of sockeye salmon) Igushik Wood 30 Nushagak Kvichak Alagnak Naknek 20 Egegik Ugashik 10 0 1960 1970 1980 1990 2000 2010 Year 83
  • Implications• 43 new SNPs are now available for sockeye salmon - Already in use 84
  • Implications• 43 new SNPs are now available for sockeye salmon - Already in use• Methods outlined are important for developing SNP panels for any system or question 85
  • Motivations• Improved fisheries management – Developing new management tools 86
  • Motivations• Improved fisheries management – Developing new management tools• Understanding salmon mortality – Characterizing variability in senescence 87
  • 88
  • Salmon Senescence• Undergo rapid senescence 89
  • 90
  • Salmon Senescence• Undergo rapid senescence 91
  • Salmon Senescence• Undergo rapid senescence• Rates of senescence vary: – in the same populations (Perrin & Irvine 1990) – between populations (Carlson et al. 2007)• Characterized by physiological trade-offs 92
  • Salmon Senescence • Characterized by physiological trade-offs • Increased energetic investment in reproduction • Starvation and stress 93Finch 1994; Gotz et al. 2005; Maldonado et al. 2002
  • Salmon Senescence • Characterized by physiological trade-offs • Increased energetic investment in reproduction • Starvation and stress • Decreased immune function • Increased oxidative stress • Central nervous system disintegration 94Finch 1994; Gotz et al. 2005; Maldonado et al. 2002
  • Objectives• Uncover driving mechanisms of senescence – Develop quantitative gene expression assays for genes associated with aging – Characterize senescent specific expression patterns in sockeye salmon 95
  • Assay Design• Selected genes based on physiological responses of interest 96
  • Assay Design • Selected genes based on physiological responses of interest • Developed 5 successful assaysGene Acesion # Response Amplicon sizeViperin (vig1) NM_001124253.1 immune 244NMDA-type glutamate receptor 1 subunit AB292234.1 memory 239olfactory marker protein 1 AB490250.1 olfactory 169telomerase reverse transcriptase (TERT) CX246542 aging 151GnRH Precursor D31868 reproduction 226 97
  • Measuring Gene Expression• 25 sockeye salmon – 11 pre-senescent – 14 senescent• Expression measured in brain tissue 98
  • Measuring Gene Expression 99
  • NMDA• Involved in synaptic plasticity and memory• Linked to neurodegenerative disorders 100
  • NMDA• Involved in synaptic plasticity P = 0.12 and memory 50 50• Linked to neurodegenerative 40 40 disorders Gene expression 30• No significant difference 30 20 20 10 10 0 0 Pre- Senescent senescent 101
  • OMP1• Olfactory marker proteins (OMP) necessary for the function of olfactory receptor neurons 102
  • OMP1• Olfactory marker proteins (OMP) necessary for the P = 0.32 function of olfactory 150 150 receptor neurons• No significant difference Gene expression 100 100 50 50 0 0 Pre- Senescent senescent 103
  • GnRHp• Part of the GnRH axis which plays a critical role in reproduction 104
  • GnRHp• Part of the GnRH axis which plays a critical role in 1000 1000 P = 0.15 reproduction 800 800• No significant difference Gene expression 600 600 400 400 200 200 0 0 Pre- Senescent senescent 105
  • Viperin• Anti-viral protein involved in the innate immune response 106
  • Viperin• Anti-viral protein involved in the innate 30000 30000 P = 0.017 immune response 25000 25000• Significant difference Gene expression 20000 20000 15000 15000 10000 10000 5000 0 5000 0 Pre- Senescent senescent 107
  • Viperin• Anti-viral protein involved in the innate 30000 30000 P = 0.017 immune response 25000 25000• Significant difference Gene expression• Immune response 20000 20000 attempted in senescent 15000 15000 salmon 10000 10000 5000 0 5000 0 Pre- Senescent senescent 108
  • TERT• Catalytic subunit of the enzyme telomerase• Responsible for telomere repair and extension 109
  • TERT• Catalytic subunit of the 80 80 enzyme telomerase P = 0.03• Responsible for telomere 60 60 Gene expression repair and extension• Significant difference 40 40 20 20 0 0 Pre- Senescent senescent 110
  • TERT• Catalytic subunit of the 80 80 enzyme telomerase P = 0.03• Responsible for telomere 60 60 Gene expression repair and extension• Significant difference 40 40• Maintaining telomere length critical to survival till 20 20 spawning 0 0 Pre- Senescent senescent 111
  • Gene Expression 4 Pre-senescent Senescent 3 2Principal component 2 (19.35 %) 1 0 -1 -2 -3 -2 0 2 4 6 8 112 Principal component 1 (61.06 %)
  • Gene Expression 4 Pre-senescent Senescent 3 2Principal component 2 (19.35 %) 1 0 -1 -2 -3 -2 0 2 4 6 8 113 Principal component 1 (61.06 %)
  • Gene Expression 4 Pre-senescent Senescent 3 1) Viperin 2 2) TERTPrincipal component 2 (19.35 %) 1 GnRHp 12 0 NMDA OMP1 -1 -2 -3 -2 0 2 4 6 8 114 Principal component 1 (61.06 %)
  • Findings• Greater expression in senescent salmon 115
  • Findings• Greater expression in senescent salmon• Greater variation in expression of senescent salmon 116
  • Findings• Greater expression in senescent salmon• Greater variation in expression of senescent salmon 117
  • Findings• Greater expression in senescent salmon• Greater variation in expression of senescent salmon• Significant differences detected for two genes: TERT (aging) and Viperin (immune function) 118
  • Conclusions• Strong response detected in immune function – Driving mechanism or associated process? 119
  • Conclusions• Strong response detected in immune function – Driving mechanism or associated process?• Telomerase activity represents senescence specific signal 120
  • Implications• New assays can be used at any stage of the sockeye salmon life cycle 121
  • Implications• New assays can be used at any stage of the sockeye salmon life cycle• Telomere dynamics important for understanding variation in rates of senescence 122
  • Telomere DynamicsPopulation 1 Population 2 123
  • Telomere Dynamics Population 1 Population 2• Fast senescence • Slow senescence 124
  • Telomere Dynamics Population 1 Population 2• Fast senescence • Slow senescence• Low telomerase • High telomerase expression expression 125
  • Implications• New assays can be used at any stage of the sockeye salmon life cycle• Telomere dynamics important for understanding variation in rates of senescence 126
  • Implications• New assays can be used at any stage of the sockeye salmon life cycle• Telomere dynamics important for understanding variation in rates of senescence – Measure of life history diversity (rate of senescence) 127
  • Motivations• Improved fisheries management – Developing new management tools• Understanding salmon mortality – Characterizing variability in senescence 128
  • AcknowledgmentsRoberts Lab: Funding:• Sam White • Alaska Sustainable Salmon Fund• Steven Roberts • Bristol Bay Regional Seafood• Emma Timmins-Schiffman Development Group• Dave Metzger • The Gordon and Betty Moore• Mackenzie Gavery Foundation • The School of Aquatic and FisherySeeb Lab: Sciences• Jim Seeb • OACIS NSF GK12• Lisa Seeb• Carita Pascal Committee:• Eleni Petrou • Thomas Quinn• Meredith Everett • Steven Roberts (Co-chair)• Wes Larson • James Seeb (Co-chair)• Marissa Jones • William Templin• Sewall Young• Ryan Waples FRIENDS and FAMILY Cohort ‘09 129
  • THANK YOU! 130
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