Progress 02/24/14 to 01/31/15
Outputs
Target Audience: Our research results were disseminated to the scientific community at large through publication of findings in the peerreviewed scientific literature. Additionally, research results were orally presented to the audience at Aquaculture America 2014 (The Annual Meeting of the World Aquaculture Society, US Chapter) in Seattle, WA, a diverse group of attendees comprised of academics, research scientists, government researchers, fish farmers, and other aquaculture support industry persons. The results from this project were presented to the international scientific community at The 10th International Symposium on Reproductive Physiology of Fishes in Olhão, Portugal. This research was showcased at the North Carolina Agriculture and Biotechnology Summit 2014 (Innovation Fair), which was attended by academic researchers, agriculture industry representatives, and other government regulatory and research personnel, including Dr. Sonny Ramaswamy (Director of the USDA NIFA). This research also was briefly discussed at the NC Aquaculture Development Conference in New Bern, NC and the NC Marine Fish Culture Workshop at the NOAA Center for Coastal Fisheries and Habitat Research in Beaufort, NC. This conference and workshop were attended by academic researchers, representatives of North Carolina SeaGrant and the North Carolina Biotechnology Center, other government regulatory and research personnel from NOAA and NMFS, and commercial aquaculture producers. The research findings also were publicized on the online (web) news pages of the North Carolina State University Department of Applied Ecology, North Carolina SeaGrant, and South Carolina SeaGrant and they were published in the North Carolina SeaGrant Coastwatch. Changes/Problems: The original principal investigator (Dr. Craig V. Sullivan) retired during the course of the project and the current principal investigator (Dr. Benjamin J. Reading) was appointed in September 2013. This lead to a delay in data transfer and analysis during the last year of the project (e.g., Objective 2). What opportunities for training and professional development has the project provided? Two post-doctoral researchers were trained during the course of this project. One of these post-doctoral researchers became principal investigator (current) of the project in 2013, when the original principal investigator retired from North Carolina State University. The second post-doctoral researcher was trained during year 2014 of the project. For both of these post-doctoral researchers, the project served as a training vehicle of novel methods (machine learning) of gene expression analysis. Additionally, the current principal investigator mentored two graduate students and one undergraduate researcher on the methods used in this study. These (or similar) machine learning methods of gene expression analysis are now being used in other studies of fish physiology, including osmoregulation of tilapia and reproduction of southern founder and white perch. Additionally, the current principal investigator served as a Faculty Mentor for Creating Awareness of Agriculture and Life Sciences Disciplines, Degree Programs and Discoveries (CAALS 3-D). This program is a North Carolina State University partnership with North Carolina School of Science and Mathematics (NCSSM). The mission of CAALS 3-D is to increase the awareness and interest in career fields within the food, agricultural, environmental and life sciences of high school students from under-represented groups. During the course of this project the current principal investigator mentored two exemplary high school students in molecular biology laboratory techniques and also educated them on the importance of aquaculture for global food security. How have the results been disseminated to communities of interest? During 2014, the research findings have been disseminated through six (6) oral presentations, panel discussions, or research summaries presented at international conferences, national conferences, local conferences, and local workshops. One (1) research article has been published in a peer-reviewed journal and another (1) article is in press. Three (3) public press releases describing the major findings of the research have been published online in news feeds and one (1) public release occurred through a North Carolina SeaGrant Coastwatch publication. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Overview Our goal is to understand the fundamentals of fish reproduction and egg quality, with the overall objective of helping to secure the hybrid striped bass industry as a major form of aquaculture in North America. Poor egg quality is a major problem in finfish Research Objectives 1) Verify that the abnormal transcript profiles associated with poor egg quality are primarily representative of the germ cells (e.g. oocytes/eggs) and are not substantially influenced by somatic tissue present in ovary biopsy samples. 3) Explore the possibility that transcriptome characteristics of poor quality eggs from domesticated (farmed) broodfish are also evident in poor quality eggs from wild striped bass.Domestic and wild striped bass females with fully-grown oocytes were biopsied for ovary tissue and induced to spawn, and those progressing normally to ovulation were assessed for egg quality evaluated as a percentage of eggs producing viable 4-h-old blastulas (range 0-92%). Levels of maternal gene transcripts were measured in domestic striped bass ovary biopsies in 2010 by microarray (11,000 genes) and in domestic and wild striped bass ovary and eggs in 2012 by RNA-Seq (30,000 genes). Artificial neural networks and supervised machine learning were used to model the profiles of gene expression and their relationship to egg quality. In the microarray study, collective changes in expression of a limited suite of genes (233) representing <2% of the queried ovary transcriptome explained >90% of the eventual variance in surviving embryos. Egg quality related to minor changes in gene expression (<0.2-fold), with most individual transcripts making negligible contribution (<1%) to overall predictions of egg quality. Thus, subtle changes in ovarian transcriptome profiles predict most of the variation in egg quality. Correlation analyses of candidate genes indicated that dysfunction of the ubiquitin-26S proteasome, COP9 signalosome and cell cycle engenders developmental incompetence in both the domestic striped bass. In our RNA-Seq studies, we evaluated the transcriptome of ovary biopsies and eggs from the same females and both were highly predictive of egg quality, but models built using data from biopsies explained slightly more (10-15%) variation in egg quality, suggesting that follicle cell and possibly other somatic transcripts may contribute to egg quality determination. Similar genes and gene pathways related to cell cycle regulation also were identified in the RNA-Seq studies providing orthogonal conformation of the microarray study. We further verified the striking predictive power of supervised machine learning models by repeating the RNA-Seq study with wild striped bass evaluated for ovary transcripts by RNA-Seq gene expression analysis and found a similar result as described above. The overall average cross validation for these machine learning models (the proportion of variation in egg quality that could be predicted) remarkably ranged from 78% to 91%. 2) Discover whether the transcriptomic dysfunction underlying poor egg quality is already evident in oocytes at early developmental stages (e.g. primary growth) present years before spawning and persists after virgin spawning as the females age. These samples have been collected and the data are still being analyzed. Principal investigator Robert W. Chapman has these data (South Carolina SeaGrant portion of this joint project with North Carolina SeaGrant). 4) Evaluate potential contributions of genotype (e.g. pedigree) to egg quality and to oocyte/egg transcriptome profiles. When pedigrees of fish in our microarray study were constructed via genotying at 48 microsatellite DNA loci, parentage had no detectable effect on egg quality, and input of digital scores for all alleles into the machine learning models did not improve their predictive ability. When viewed at finer genetic scale, a Mendelian component to early developmental failure might become apparent, but presently the proximal cause(s) of the transcriptomic lesion underlying poor egg quality remains unresolved. It seems likely that epigenetics may contribute to the egg transcriptome defects and the degraded developmental potential.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2014
Citation:
Chapman*, R.W., Reading*, B.J., and Sullivan*, C.V. 2014. Ovary transcriptome profiling via artificial intelligence reveals a
transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis. PLoS ONE 9(5):e96818. *All authors
contributed equally to this manuscript.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2015
Citation:
Sullivan, C.V., Chapman, R.W., Reading, B.J., and Anderson, P.E. Accepted. Transcriptomics of egg quality in teleost fish:
New developments and future directions. Submitted to General and Comparative Endocrinology.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
B.J. Reading, J.D. Shilling, and H.V. Daniels. 2014. Use of molecular and genomic methods to optimize the farming of
fishes. North Carolina Agriculture and Biotechnology Summit 2014, Innovation Fair. November 18-19. North Carolina State
University, Jane S. McKimmon Center, Raleigh, NC, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
Hinshaw, J. Reading, B.J., and Watanabe, W.O. 2014. Research Panel. The NC Marine Fish Culture Workshop.
November 20-21. The NOAA Center for Coastal Fisheries and Habitat Research, Beaufort, NC, USA.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2014
Citation:
Reading, B.J., Chapman, R.W., Sullivan, C.V., and Anderson, P.E. 2014. Presented by B.J. Reading. Ovarian
transcriptome reliably predicts egg quality in wild and domesticated striped bass Morone saxatilis Part II. Aquaculture
America 2014: Taking Aquaculture to New Heights Through Technology, Marketing, Collaboration. February 9-12, 2014.
Seattle, WA, USA.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2014
Citation:
Sullivan, C.V., Chapman, R.W., Reading, B.J., and Anderson, P.E. 2014. Ovarian transcriptome reliably predicts egg
quality in wild and domesticated striped bass (Morone saxatilis). Invited State-of-the-art Lecture. The 10th International
Symposium on Reproductive Physiology of Fish, Expanding the knowledge base of reproductive success: from genes to
the environment, May 25-30. Olh�o Municipal Auditorium, Olh�o, Portugal.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2014
Citation:
Sullivan, C.V., Chapman, R.W., Reading, B.J., and Anderson, P.E. 2014. Presented by C.V. Sullivan. Ovarian
transcriptome reliably predicts egg quality in wild and domesticated striped bass Morone saxatilis Part I. Aquaculture
America 2014: Taking Aquaculture to New Heights Through Technology, Marketing, Collaboration. February 9-12, 2014.
Seattle, WA, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
Hinshaw, J. and W. Watanabe. 2014. North Carolina State Aquaculture Research Update. NC Aquaculture Development
Conference. February 21-22, 2014. New Bern, NC.
- Type:
Other
Status:
Published
Year Published:
2014
Citation:
Gene Groups Key to Embryo Development. North Carolina SeaGrant Coastwatch, Summer 2014, Issue 3, Page 30. North
Carolina SeaGrant, Raleigh, North Carolina.
- Type:
Other
Status:
Published
Year Published:
2014
Citation:
Study Highlights Role of Gene Groups in Embryo Development. South Carolina SeaGrant Consortium New Releases,
South Carolina Sea Grant Consortium, Charleston, South Carolina. May 12, 2014.
http://www.scseagrant.org/content/?cid=19
- Type:
Other
Status:
Published
Year Published:
2014
Citation:
Small Group of Genes Influence Egg Quality. North Carolina SeaGrant News, North Carolina SeaGrant, Raleigh, North
Carolina. May 12, 2014. http://ncseagrant.ncsu.edu/blog/news/small-group-of-genes-influence-egg-quality/
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Progress 02/24/14 to 09/30/14
Outputs
Target Audience: Our research results were disseminated to the scientific community at large through publication of findings in the peer-reviewed scientific literature. Additionally, research results were orally presented to the audience at Aquaculture America 2014 (The Annual Meeting of the World Aquaculture Society, US Chapter) in Seattle, WA, a diverse group of attendees comprised of academics, research scientists, government researchers, fish farmers, and other aquaculture support industry persons. The results from this project were presented to the international scientific community at The 10th International Symposium on Reproductive Physiology of Fishes in Olhão, Portugal. This research was showcased at the North Carolina Agriculture and Biotechnology Summit 2014 (Innovation Fair), which was attended by academic researchers, agriculture industry representatives, and other government regulatory and research personnel, including Dr. Sonny Ramaswamy (Director of the USDA NIFA). This research also was briefly discussed at the NC Aquaculture Development Conference in New Bern, NC and the NC Marine Fish Culture Workshop at the NOAA Center for Coastal Fisheries and Habitat Research in Beaufort, NC. This conference and workshop were attended by academic researchers, representatives of North Carolina SeaGrant and the North Carolina Biotechnology Center, other government regulatory and research personnel from NOAA and NMFS, and commercial aquaculture producers.The research findings also were publicized on the online (web) news pages of the North Carolina State University Department of Applied Ecology, North Carolina SeaGrant, and South Carolina SeaGrant and they were published in the North Carolina SeaGrant Coastwatch. Changes/Problems: The original principal investigator (Dr. Craig V. Sullivan) retired during the course of the project and the current principal investigator (Dr. Benjamin J. Reading) was appointed in September 2013. This lead to a delay in data transfer and analysis during the last year of the project (e.g., Objective 2). What opportunities for training and professional development has the project provided? Two post-doctoral researchers were trained during the course of this project. One of these post-doctoral researchers became principal investigator (current) of the project in 2013, when the original principal investigator retired from North Carolina State University. The second post-doctoral researcher was trained during year 2014 of the project. For both of these post-doctoral researchers, the project served as a training vehicle of novel methods (machine learning) of gene expression analysis. Additionally, the current principal investigator mentored two graduate students and one undergraduate researcher on the methods used in this study. These (or similar) machine learning methods of gene expression analysis are now being used in other studies of fish physiology, including osmoregulation of tilapia and reproduction of southern founder and white perch. Additionally, the current principal investigator served as a Faculty Mentor for Creating Awareness of Agriculture and Life Sciences Disciplines, Degree Programs and Discoveries (CAALS 3-D). This program is a North Carolina State University partnership with North Carolina School of Science and Mathematics (NCSSM). The mission of CAALS 3-D is to increase the awareness and interest in career fields within the food, agricultural, environmental and life sciences of high school students from under-represented groups. During the course of this project the current principal investigator mentored two exemplary high school students in molecular biology laboratory techniques and also educated them on the importance of aquaculture for global food security. How have the results been disseminated to communities of interest? During 2014, the research findings have been disseminated through six (6) oral presentations, panel discussions, or research summaries presented at international conferences, national conferences, local conferences, and local workshops. One (1) research article has been published in a peer-reviewed journal and another (1) article is in review. Three (3) public press releases describing the major findings of the research have been published online in news feeds and one (1) public release occurred through a North Carolina SeaGrant Coastwatch publication. What do you plan to do during the next reporting period to accomplish the goals? We will prepare the final report of the project in the remaining duration of the project and also prepare the following manuscript for publication (related to the project): Reading, B.J., Williams, V.N., Chapman, R.W., Islam Williams, T., Schilling, J., and Sullivan, C.V. In preparation. Proteomics of striped bass (Morone saxatilis) egg quality evaluated by machine learning.
Impacts
What was accomplished under these goals?
Overview Our goal is to understand the fundamentals of fish reproduction and egg quality, with the overall objective of helping to secure the hybrid striped bass industry as a major form of aquaculture in North America. Poor egg quality is a major problem in finfish aquaculture that has remained intractable and causally unknown despite decades of research worldwide. We applied recent advances in machine learning data analysis to better understand the problem of reproductive failure in domesticated striped bass broodstock and to explore the potential utility of our findings for management of wild fish populations. Research Objectives 1) Verify that the abnormal transcript profiles associated with poor egg quality are primarily representative of the germ cells (e.g. oocytes/eggs) and are not substantially influenced by somatic tissue present in ovary biopsy samples. 3) Explore the possibility that transcriptome characteristics of poor quality eggs from domesticated (farmed) broodfish are also evident in poor quality eggs from wild striped bass. Domestic and wild striped bass females with fully-grown oocytes were biopsied for ovary tissue and induced to spawn, and those progressing normally to ovulation were assessed for egg quality evaluated as a percentage of eggs producing viable 4-h-old blastulas (range 0-92%). Levels of maternal gene transcripts were measured in domestic striped bass ovary biopsies in 2010 by microarray (11,000 genes) and in domestic and wild striped bass ovary and eggs in 2012 by RNA-Seq (30,000 genes). Artificial neural networks and supervised machine learning were used to model the profiles of gene expression and their relationship to egg quality. In the microarray study, collective changes in expression of a limited suite of genes (233) representing <2% of the queried ovary transcriptome explained >90% of the eventual variance in surviving embryos. Egg quality related to minor changes in gene expression (<0.2-fold), with most individual transcripts making negligible contribution (<1%) to overall predictions of egg quality. Thus, subtle changes in ovarian transcriptome profiles predict most of the variation in egg quality. Correlation analyses of candidate genes indicated that dysfunction of the ubiquitin-26S proteasome, COP9 signalosome and cell cycle engenders developmental incompetence in both the domestic striped bass. In our RNA-Seq studies, we evaluated the transcriptome of ovary biopsies and eggs from the same females and both were highly predictive of egg quality, but models built using data from biopsies explained slightly more (10-15%) variation in egg quality, suggesting that follicle cell and possibly other somatic transcripts may contribute to egg quality determination. Similar genes and gene pathways related to cell cycle regulation also were identified in the RNA-Seq studies providing orthogonal conformation of the microarray study. We further verified the striking predictive power of supervised machine learning models by repeating the RNA-Seq study with wild striped bass evaluated for ovary transcripts by RNA-Seq gene expression analysis and found a similar result as described above. The overall average cross validation for these machine learning models (the proportion of variation in egg quality that could be predicted) remarkably ranged from 78% to 91%. 2) Discover whether the transcriptomic dysfunction underlying poor egg quality is already evident in oocytes at early developmental stages (e.g. primary growth) present years before spawning and persists after virgin spawning as the females age. These samples have been collected and the data are still being analyzed. Principal investigator Robert W. Chapman has these data (South Carolina SeaGrant portion of this joint project with North Carolina SeaGrant). 4) Evaluate potential contributions of genotype (e.g. pedigree) to egg quality and to oocyte/egg transcriptome profiles. When pedigrees of fish in our microarray study were constructed via genotying at 48 microsatellite DNA loci, parentage had no detectable effect on egg quality, and input of digital scores for all alleles into the machine learning models did not improve their predictive ability. When viewed at finer genetic scale, a Mendelian component to early developmental failure might become apparent, but presently the proximal cause(s) of the transcriptomic lesion underlying poor egg quality remains unresolved. It seems likely that epigenetics may contribute to the egg transcriptome defects and the degraded developmental potential. Key Outcomes A change in knowledge We discovered that a broad spectrum of maternal gene transcripts deposited in eggs form a discrete transcriptomic fingerprint highly predictive of and likely to regulate egg quality and reproductive fitness in a fish. Extension of our results from farmed fish to wild stocks has important implications for fishery management as they indicate clear and predictable differences in fertility of female fish. Microarray or RNA-Seq studies generate massively parallel data sets that are not entirely compatible with traditional statistics (e.g., regression, ANOVA). Inherent loss of degrees of freedom and rise in required sample sizes accompanies the analysis of such data sets. To circumvent this problem, standard analysis packages typically employ multiple test corrections to control false discovery rate. However, this approach treats the transcriptome as an assemblage of independent components and not as a highly interactive system. To address this analytical challenge, we can reverse the usual practice of fitting data to preconceived models, instead extracting models from the data with machine learning tools. Here we ground truth a revolutionary approach to analysis of gene expression data using such machine learning. We consider this approach to be “transcriptomic fingerprinting”. These data and methods overview have been published in one manuscript (2014) and are also reported in a second manuscript currently in review. A change in action Given the strengths of machine learning data analysis, we have employed these “fingerprinting” machine learning methods developed during the research project in two non-related tandem mass spectrometry quantitative proteomics studies published in 2013 and 2014 and an additional manuscript that is presently in review. Two more manuscripts employing machine learning analysis of RNA-Seq gene expression and proteomics data also are in preparation for submission to peer-reviewed journals. Therefore, machine learning data analysis is applicable to microarry data, RNA-seq data, and proteomics data. The current principal investigator has trained two graduate students and one post-doc in these machine learning methods leading to these research publications. A change in condition Response to these results are already being implemented in the National Program for Genetic Improvement and Selective Breeding for the Hybrid Striped Bass Industry. The offspring of striped bass with low fertility (<5%) are no longer being considered for rearing in this program and are being culled such that this trait is no longer propagated. Although there is no overt genetic effect on egg quality as shown in the project, epigenetic effects are implicated and further research will be required to identify husbandry practices that can mitigate these egg quality problems. It may be possible to employ transcriptome profiling to identify striped bass for breeding, and the results of this project suggest that transcriptome profiling may offer improved resolution of functional maturity schedules in wild stocks. We predict that, as the research accelerates and the price of next-generation DNA sequencing plummets, such 'transcriptome-assisted' selective breeding and fishery management may start to become routine over the course of the next decade.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2014
Citation:
Chapman*, R.W., Reading*, B.J., and Sullivan*, C.V. 2014. Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis. PLoS ONE 9(5):e96818. *All authors contributed equally to this manuscript.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2015
Citation:
Sullivan, C.V., Chapman, R.W., Reading, B.J., and Anderson, P.E. In review. Transcriptomics of egg quality in teleost fish: New developments and future directions. Submitted to General and Comparative Endocrinology.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
B.J. Reading, J.D. Shilling, and H.V. Daniels. 2014. Use of molecular and genomic methods to optimize the farming of fishes. North Carolina Agriculture and Biotechnology Summit 2014, Innovation Fair. November 18-19. North Carolina State University, Jane S. McKimmon Center, Raleigh, NC, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
Hinshaw, J. Reading, B.J., and Watanabe, W.O. 2014. Research Panel. The NC Marine Fish Culture Workshop. November 20-21. The NOAA Center for Coastal Fisheries and Habitat Research, Beaufort, NC, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
Reading, B.J., Chapman, R.W., Sullivan, C.V., and Anderson, P.E. 2014. Presented by B.J. Reading. Ovarian transcriptome reliably predicts egg quality in wild and domesticated striped bass Morone saxatilis Part II. Aquaculture America 2014: Taking Aquaculture to New Heights Through Technology, Marketing, Collaboration. February 9-12, 2014. Seattle, WA, USA.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2014
Citation:
Sullivan, C.V., Chapman, R.W., Reading, B.J., and Anderson, P.E. 2014. Ovarian transcriptome reliably predicts egg quality in wild and domesticated striped bass (Morone saxatilis). Invited State-of-the-art Lecture. The 10th International Symposium on Reproductive Physiology of Fish, Expanding the knowledge base of reproductive success: from genes to the environment, May 25-30. Olh�o Municipal Auditorium, Olh�o, Portugal.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2014
Citation:
Sullivan, C.V., Chapman, R.W., Reading, B.J., and Anderson, P.E. 2014. Presented by C.V. Sullivan. Ovarian transcriptome reliably predicts egg quality in wild and domesticated striped bass Morone saxatilis Part I. Aquaculture America 2014: Taking Aquaculture to New Heights Through Technology, Marketing, Collaboration. February 9-12, 2014. Seattle, WA, USA.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2014
Citation:
Hinshaw, J. and W. Watanabe. 2014. North Carolina State Aquaculture Research Update. NC Aquaculture Development Conference. February 21-22, 2014. New Bern, NC.
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