Source: UNIVERSITY OF MAINE submitted to
ESTABLISHING METRICS OF BROODSTOCK AND OFFSPRING QUALITY IN ATLANTIC SALMON AQUACULTURE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1028336
Grant No.
2022-67016-37226
Project No.
ME002021-06794
Proposal No.
2021-06794
Multistate No.
(N/A)
Program Code
A1211
Project Start Date
Jul 1, 2022
Project End Date
Jun 30, 2027
Grant Year
2022
Project Director
Hamlin, H.
Recipient Organization
UNIVERSITY OF MAINE
(N/A)
ORONO,ME 04469
Performing Department
School of Marine Sciences
Non Technical Summary
The ability to identify the optimal fish for breeding (termed broodstock), that will result in high performing offspring is a cornerstone of any sustainable aquaculture industry. This project seeks to identify characteristics in broodstock or their offspring that will predict offspring quality and future performance, allowing farmers to optimize broodstock selection, and better manage their stocks. Therefore, the objectives of this project are to 1) develop metrics that will identify broodstock that will have a high likelihood of producing high quality offspring and 2) identify metrics that predict survival and growth of embryos and juveniles to inform hatchery managers of what growth models to plan for. These metrics can be used for the eventual development of on-site tools farm managers can use to identify the best fish to use for breeding, and to predict the growth performance of offspring. This project will increase our understanding of biological factors underlying reproductive performance, and could improve the economic viability of the Atlantic salmon aquaculture industry.
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
30137991020100%
Goals / Objectives
Aim 1: Examine the metabolome and transcriptome of broodstock female salmon and determine their ability to predict broodstock and offspring quality.Broodstock selection and the control of egg quality is a critical aspect of any sustainable finfish aquaculture industry. Increasing our ability to select optimally performing broodstock, and improve our understanding of the regulation of reproductive processes will have significant improvements across the production process.Aim 2: Examine the metabolome and transcriptome of adult male Atlantic salmon and determine their ability to predict early maturation.Early male maturation results in a significant financial loss to the industry, as these males must be culled often after prolonged periods of culture (>1yr). The mechanisms of early maturation are poorly understood, and the ability to cull these males as early in the process as possible would result in significant cost savings. Moreover, there is an urgent need to increase our understanding of the factors governing the shift from normal to precocious maturation.Aim 3: Examine the utility of morphometrics and behavior as predictive indicators of cohort quality in embryos or yolk-sac fry.The ability to predict offspring performance as early in the process as possible (e.g. as embryos or young fry) would enable hatchery managers to more efficiently cull poor performing batches, and more accurately plan for the sale of surplus eggs. Further, the identification of biomarkers of quality may increase our understanding of the mechanisms underpinning performance.
Project Methods
Methods:Experiment 1: Examine the metabolome and transcriptome of broodstock female Atlantic salmonand use AI/machine learning to predict quality and progeny success.Fish from both years of collections will be combined into either the high embryo survival rate (n = 20 for each tissue) or low survival rate (n = 20 for each tissue) with comparison to cull (n=20, ten from each sampling year) for metabolomic analysis. Metadata (including morphometrics) for all individual fish will be collected for AI analysis in conjunction with metabolic profiles.?Three-year-old, female Atlantic salmon from the USDA NCWMAC will besampled at spawning (n = 150/yr). Using the established protocols from USDA, fish areanesthetized in tricaine methane-sulfonate (MS-222) for 15 min and removed from the holdingtank for sampling. Mucus sampling is conducted using NIST validated methods. Samples from year one collections that are not earmarked for broodstock quality will be run through the standard NIST protocols for new matrix assessment to validate ovarian fluid as a viable sample type for NMR metabolomics.Automatic machine learning (Auto-ML) approaches will also be leveraged to search foroptimal classifiers and preprocessing steps (feature engineering and potentially dimensionalityreduction) to yield the best possible classifiers given the data. Models examined will include, butare not limited to: (deep) neural networks, tree-based models (gradient-boosted trees, randomforests), partial least squares and other projection methods, kernel-based support vectorclassifiers, and ensemble approaches. These will be trained to predict female quality as anoutput, given the NMR input. Prediction accuracies and confusion matrices are two keyperformance indicators, but the inner workings of the model may also reveal decision pathwaysthat are critical to understanding the connection between the metabolome and progeny success.To this end, we will then leverage SHAP (SHapley Additive eXplanations) to provide a scientificinterpretation and inspection of these black-box models to uncover any connection to underlyingbiomarkers.Transcriptomic Analysis:RNA will be isolated from mucus samples with a Qiagen RNeasy Mini kit, including optionalon-column DNase I treatment. RNA quality and quantity will be quantifiedspectrophotometrically and samples will be stored at -80C. Samples will be sent to theHudsonAlpha Institute for Biotechnology Genomic services lab, or a related facility, for librarypreparation and sequencing.Experiment 2: Examine the metabolome and transcriptome of male Atlantic salmon and useAI/machine learning to predict early maturation.In year one of this study, mucus and plasma will be collected from 120 two-year-old males.These fish are pit tagged and can be followed to the following year. In year two of this study,during a cull event, mucus and plasma from 30 precocious males and 30 normal males will besampled, specifically targeting precious males that were sampled in year 1. Metabolomicanalysis will be conducted on all two-year old samples, and the cull event samples fromindividuals sampled in year 1 (n = 20 for each tissue type (mucus and plasma, treatment, andtimepoint meaning a total of 160 samples from 40 individuals). Remaining samples from bothtime points will be used to generate QC pools as outlined in the broodstock comparison.Mucus and plasma samples will follow the same protocols as in experiment 1.Metabolic signatures in blood plasma indicative of early maturation are expected to correlate todistinctive markers within the more practical in-field sample collection (skin mucus).Experiment 3: Examine a suite of morphometrics in developing salmon embryos and yolk-sacfry to determine their predictive capacity for survival and future growth performance.Image stacking:To create images of optimal clarity for analysis, embryos and larvae will be placed in glass petridishes inside a 3D muted lightbox. This will prevent light shine that may obscure image detail. A high resolution digital camera with macro lens will be used in conjunction with a StackShot macrorail package.Morphometric measurements:Two embryonic stages that will be targeted for these analyses are stage t72 (mid somitogenesis)and the development of pigmented eyes. The egg will be oriented such that the embryo is centered in the vertical plane. Measurements will be taken from the median line to the edge of the circumorbital region, and from the median line to the midline of the eye on both right and left sides. For each measurement, midline asymmetry (Ma) will be calculated as the absolute difference between the distance from the midline to the measured parameter for both the right (R) and left (L) sides, using the following formula Ma= R-L .

Progress 07/01/22 to 06/30/23

Outputs
Target Audience:Target audiences reached during this reporting period includes industry professionals, federal and state agencies, and the public at large (via conference presentations and in-person meetings). Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This project has allowed for networking / partnering with other institutions, such as the USDA and University of Maine faculty members. USDA staff members with specialties in reproductive endocrinology and nutrition, as well as staff technicians have been essential in the data collection, field work, and data dissemination. Working with the USDA has provided professional development opportunities through field work; harvesting Atlantic salmon in Machiasport at the Cooke facility to obtain carcass weight metrics for the selective breeding program. Completing field work for the USDA's annual spawns has shed light on industry processing methods; data collection, stripping, fertilizing, blood collection, mucus collection, and the collaborative teamwork required to successfully complete industry tasks. This project has fostered connections to be made with faculty and graduate students in different disciplines at the University of Maine. Dr. Emma Perry, an electron microscopist, has provided professional development in offering imaging guidance and ideas regarding the morphometric work with small embryos and alevin. Working with Emma has allowed familiarity to be gained in areas of microscopy, fixative, and histological techniques. Images have been taken with a scanning electron microscope, where imaging shows surface and cell composition in great detail. Additionally, conversations with University of Maine's developmental biologist Dr. Jared Talbot have allowed professional development through training with zebrafish standard operating procedures. Developmental biology exposure has offered the opportunity for methodologies to be integrated into our project scope to better understand embryo mortality, and identify indicators of success early on in the aquaculture process. How have the results been disseminated to communities of interest?Results were shared at a two day graduate student symposium held May 8th-9th at the Darling Marine Center, Walpole, Maine. This was a professional development retreat hosted by the University of Maine's School of Marine Sciences. The audience consisted of faculty and fellow SMS graduate students. The poster presented was titled; "Establishing predictive metrics of Atlantic salmon". Results outlining the eye up rates and mortality in comparison to the USDA's breeding values were presented, showing no correlation. The poster results elucidated the embryo mortality issue within the industry from 2008-present. Emphasis on the breeding values was presented to amplify the idea that selecting for growth and disease resistance, leaves the breeding program with an energy trade-off, that may be apparent with the lack of optimum embryo survival. Progress displaying images of eggs and alevin for morphometric study were presented. Future direction of investigating the genome of eggs through transcriptomic analyses was explained. The utility of morphometrics and fluctuating asymmetry as a predictive metric of embryo quality was disseminated to classmates within graduate school courses SMS 691(Marine science seminar), and SMS 500 (Marine Biology) in the form of elevator pitches, and a project proposal presentation. Results for Aim 1 were presented at a national conference, heavily attended by industry personnel? Hamlin, H.J., Bair, H., Peterson, B., Burr, G., Legacki, E., Zohar, Y., Miller, J., Stubblefield, J., Firkus, T., Improving year-round egg production for Atlantic salmon grown in RAS. RASTECH, April, 2023, Orlando, FL. What do you plan to do during the next reporting period to accomplish the goals?Aim 1; In addition to continuation of mucus swabs, ovarian fluid, and blood sampling for omics analysis, we have planned to take metabolomic samples of embryos, and yolk-sac fry in sync with the developmental experiment time points. Samples of eggs amounting to 0.1 g will be collected at multiple time points: prior to fertilization, post fertilization, and every other day until reaching yolk-sac fry stage. Using the liquid chromatography mass spectrometry (LC-MS-MS) we will run samples to gain understanding of the present sex steroids and activity at each stage of development. Correlation of steroids to batch quality will be key to understanding biological intricacies throughout development, and pose the opportunity to be incorporated as predictive metrics of future offspring performance. Aim 2 - We intend to focus on this aim in year 3. Aim 3; we plan to collect eggs from the USDA's 2023 spawning broodstock at the Cooke Bingham Maine facility. The collection will follow suit with last year; eggs will be raised at the DRL, University of Maine campus. Experimental design will ensure equal representation of all EBV's (control, low, medium, and high) for proper replication. The 2023 egg experiment will maintain incubation temperatures at industry standard to make findings applicable in production settings. This fall we will integrate developmental biology aspects to the incubation portion of the experiment allowing for in depth examination of the morphometrics changes that take place from egg, embryo, to yolk-sac fry. We plan to assess morphology during cleavage, gastrulation, and assess the relationship of morphology to survival to aid in understanding the quality of egg batches. Integrating developmental biology into the experimental design will be helpful in understanding early signs of batch quality (growth, survival) that can be incorporated into predictive indicators for future performance of cohorts. Morphometric measurements of total length, yolk diameter, yolk volume, eye diameter, will continue to be recorded for individuals.This 2023 spawn collection we Intend to take a bulk of measurements on live individuals, as opposed to preserved individuals to maintain biological integrity of a living individual. Measurements and analysis with a developmental biology lens will propel our understanding of mechanisms influencing early performance, and ultimately can be incorporated into predictive biomarkers early in the production process.

Impacts
What was accomplished under these goals? Aim 1: Samples (n = 120) of eggs, skin mucus, and ovarian fluid were take in the fall 2022 spawning season from the USDA's National Coldwater Marine Aquaculture Center (NCMAC), Franklin, Maine and were shipped to NIST for metabolome assessment. NIST personnel are currently processing these samples. Plasma samples were validated for LC-MS-MS hormone assessment, and samples from the fall 2022 spawning season will be processed in the summer of 2023. Aim 2: Nothing to report Aim 3: Fifty three batches of eggs were collected. The batches of eggs were collected from mid-November 2022 to mid-December 2022 and incubated on The University of Maine's campus until hatching. Samples were collected throughout the incubation period when eye pigmentation became present (eye-up) and when the organism hatched from the chorion and was attached to the yolk-sac, at alevin stage. A total of 496 eyed eggs were preserved, and 466 post hatch alevin in a 10% formalin preservative. Samples have been imaged with a Nikon DSFi3 camera and the NIS elements imaging software through the Nikon basic research package. This software has extended depth focus (EDF) which allows the fine focus to stack 3 dimensional depths into a resulting 2d image. To grasp batch morphometrics 2 images at the eyed stage have been taken; 1 of each eyed egg individual to measure size attributes: egg area.The second image of an individual was taken with the chorion removed to gain understanding of qualitative attributes; eyes, notochord, and deformities that may exist. At the alevin stage 5 images have been taken for each individual. One image was taken of the left whole organism, and another of the right whole organism to record right and left eye diameters, total length, head depth. Images 3 and 4 were taken from the left and right of the yolk being removed. Image 5 was taken of the individual ventrally to see down the notochord from above to record fluctuating asymmetry metrics from notochord to left and right features, such as eyes, nares. Measurements have been taken through the software, and maintained in metadata to be compared to cohort parent weight, and growth metrics from the USDA. Eye up rates for batches of eggs raised at the University of Maine were calculated. Our overall eye-up was very low averaging about 20% survival, synonymous to what was seen in industry. Fifteen of the batches ranged in eye up from 1-10% and 16 batches ranged from 11%-80%, where a majority lied within the 25% eye up range.The remaining 22 batches showed zero eye up. The estimated breeding values (EBV) which is an average of the mother and father's breeding values were averaged and determined to be low, medium, or high. The values of batch EBV show no correlation to the percent embryo survival. Once all images and measurements are recorded for all batches further analysis will reveal correlation to growth and survival when comparing eggs raised at NCMAC to our morphometric findings.

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