Introduction
Sturgeon brood stock management in aquaculture and in conservation programs for endangered species will benefit from the improved staging of fish by maturity. Accurately predicting maturity is also commercially important in aquaculture and stock management if a uniform and consistent egg size and firmness are to be obtained and caviar yields are to be optimized. Optimizing harvest time could extend the caviar production season.
Currently, the only means to assess ripeness of white sturgeon (Acipenser transmontanus, Acipenseridae) females and the proper time of harvest is measurement of oocyte polarization index (PI) requiring a surgical biopsy. Oocyte PI is a ratio of the distance of the germinal vesicle from the animal pole to the oocyte animal-vegetal axis diameter. PI indicates morphogenetic changes in the ovarian follicle occurring during late vitellogenesis and leading to maturational competence. This technique is accurate but invasive and stressful, time consuming, and not an effective tool for handling a large number of fish. When applied repeatedly, surgical biopsy often results in decreasing caviar yield and quality due to ovarian follicular atresia, the phagocytosis of ovarian eggs. Females undergoing the first stage of atresia may appear to be normal at harvest; but once processed, the caviar may have to be downgraded due to egg softness and its yield severely diminished due to the loss of eggs bursting during processing and handling. Even the early stage of atresia causes a reduction in the firmness, flavor, and shelf life of caviar.
To improve harvest prediction, we need to have a better understanding of the biochemical and physiological changes during ovarian maturation and how these changes correlate with roe quality. We also need new methods to predict sturgeon maturity as an alternative to the ovarian biopsy and oocyte PI measurement. New methods should be less invasive, preferably non-invasive and minimally stressful to fish, and quick. Ideally, they would allow female fish to be sorted in the fall segregating the fish that are in late vitellogenesis from other fish based upon the degree of ripeness so that fish 65 of the appropriate stage of maturity can be harvested during the winter and spring.
Infrared spectroscopy provides a unique advantage of simple sample preparation while retaining satisfactory precision and sensitivity. It is gaining wider use in chemical analysis and in nondestructive applications. In our previous work, Fourier transform infrared (FT-IR) spectroscopy (4000-400 cm-1) was employed to investigate the biochemical composition of sturgeon plasma and the spectral features could be partially related to sturgeon reproductive maturity at different stages. Other research indicates that sex steroids and vitellogenin (VTG) in sturgeon blood plasma serve as suitable biomarkers for predicting sturgeon maturity. Spectra reflect important chemical information in plasma, including proteins, lipids, polysaccharides, nucleic acids, and constituents important for fish reproduction, such as vitellogenin (VTG) and sex steroids. The raw FT-IR spectral features of white sturgeon plasma were characterized in earlier studies. The relationship between an ovarian histological prediction of sturgeon maturity and the concentration of biochemical compounds in plasma can also be determined. The major focus of this study was to describe the biochemical characterization of sturgeon plasma and caviar. The aim in the current study was to establish that reliable chemometric models could be developed for field application at different production locations (in this case 2 states (California and Idaho) in which the culture conditions for the fish were significantly different) and across several harvest years. The significance of this work was to lop robust prediction that could be used at multiple harvest locations and from year to year. Therefore, it would be best to incorporate as many samples as possible into the study, creating some overlap. Due to this, current study presents the prediction models of oocyte PI from plasma spectral features from 543 female sturgeon harvested over a four year period to provide a new means to monitor sturgeon maturity.
Results
Determining spectral reproducibility
The reproducibility of FT-IR spectra from three independent experiments were calculated using the Pearson coefficient (expressed as Dy1y2 value). Here the Dy1y22 value of FT-IR spectra for specific wavenumber regions are presented along with a combination of wavelength regions (Table 2). Mean Dy1y2 values between 7 and 10 are considered to be normal.
Segregating sturgeon by sexual maturity based upon spectral analyses
Sturgeon of different maturity were clearly segregated from each other (P<0.05) through a correlation of spectral features with PI values (Figure 2) and could be differentiated into PI ranges or groupings commonly used in the industry for segregating fish. As others have found for two-dimensional (2D) cluster analyses it was not possible to clearly segregate fish by maturity a common phenomenon observed with biological samples and a 3-dimensional (3D) model was necessary to discern small changes in spectral features between fish with similar PI. This model (Figure 2) was established using 7- and 8-year old fish in California and 15- to 21-year old fish in Idaho sampled over three years (Period I and II) and validated using fish of sampled during Period III (N=52). A dendrogram analysis, also based upon principal components was established to segregate sturgeon based upon different maturity levels (oocyte PI ranges) (Figure 3) also using fish from Period I and II and validated with fish from Period III.
Fish with low PI values of 0.05 or 0.1 could be easily segregated 10from fish with higher PIs (0.15, 0.2 or 0.25) using a single principal component (PC 1). This model may be more robust than the 3D PCA (Figure 2) because only a single principal component was necessary for its construction. It was possible to validate oocyte PI values of 0.05, 0.1, 0.15, 0.20 and 0.25 as critical points for segregating sturgeon with PI values of 0.1, 0.15 and 0.20. Many combinations of segregation models were evaluated during model establishment and we confirmed that the dendrogram model presented here (Figure 3) could provide the best segregation of the dendrogram analysis models tested. A third type of model for segregating fish based upon spectral features was a class analog or SIMCA model. This model can be used to determine if a certain fish, based upon its blood plasma spectral features would fall within a predetermined range of PI values. Using this model, fish could be correctly classified 90 per cent of the time based on plasma spectral features alone (Table 3).
Loading plot analysis
Loading plot analysis was performed to determine which major components in plasma provided the most significant (P<0.05) contribution to sample segregation (Figure 4). The first three PCs can explain over 80 per cent of segregation capability of either the cluster or dendrogram models (Figures 2 and 3) with the most significant features being associated with changes to protein secondary structure: amide I (1695 cm-1); α-helical structures (1655 cm-1); amide II (1525 cm-1); C=C, deformation C-H of amide II (1487 cm-1); symmetric CH3 bending modes of the methyl groups of proteins (1399 cm-1); CO stretching of the C-OH groups of serine, threonine and tyrosine in the cell proteins (1172/1173 cm-1).
For carbohydrate, the band at 1430 cm-1 is assigned to δ (CH2) of polysaccharide, while the band at 1205 cm-1 is dominated by the ring vibrations of polysaccharides C-O-P, PO-P. The band at 1469 cm-1 is derived from CH2 banding of the acyl chains from phospholipids. The band at 1620 cm-1 is assigned to stretching base carbonyl and ring breathing mode of 11 nucleic acids, and 1592 cm-1 is assigned to C=N and NH2 in adenine. These features are principally associated with protein and lipid deposition as ovarian follicles mature, with carbohydrate features likely associated with changes in levels of vitellogenin (VTG).
Application of PLSR models for oocyte PI prediction
PLSR models established a correlation between plasma spectral features and morphological characteristics of ovarian follicles. The appendix provides programming of the PLSR model in Matlab used for this chemometric analysis based on spectral features and includes calibration, cross validation and prediction (Appendix I). Two PLSR models were established (Figure 5, Tables 4 and S1): model A and model B. For model A, randomly selected fish sampled in 2007, 2008 and 2009 were used for model establishment. A leave-one-out cross validation was performed to challenge the rigorousness of calibrated model and then the PI values of fish samples collected in 2010 were predicted based upon the validated calibration model (Figure 5 model A, a prediction model). For a second model (model B, a cross-validation model), fish from Period I and II and 5 fish from Period III were randomly selected and used for model establishment and leave-one-out cross validation was performed to challenge the rigor of this calibration model. The PI values of all remaining fish collected during Period III were predicte based upon the validated calibration model (Figure 5 model B) with the accuracy of these predictions shown in Table S1.
The key parameters of PLSR models are summarized in Table 4, including range, number of samples, latent variables, standard error of calibration and cross validation, and regression coefficient of calibration and cross validation. PLSR model B provided a higher correlation than model A due to a greater incorporation of spectral features for fish collected in 2010. The prediction results for both PLSR models are summarized in Table S1. The standard deviation for model A is 0.03 and relatively small for an oocyte PI reference value around 0.2 or 0.3, but relatively high for PI around 0.1 (~30 per cent) which is a 12 critical value for harvest quality as a prediction of pre-ovulatory follicular atresia. 249 The standard deviation of model B is close to 0.01, which is suitable even if the reference PI value is around 0.1 (Tables 4 and S1). Taken together, the models were equally applicable to unrelated white sturgeon stocks raised at two separate locations, California and Idaho, under different environmental conditions.
Discussion
Research has been conducted to investigate the reproductive physiology of sturgeon due in part to conservation and restoration activities for threatened and endangered species but also in support of efforts to improve aquaculture practices for these fish. International demand for sturgeon caviar is focusing attention of scientists on improvement to sturgeon aquaculture that has recently become a major source for caviar production.
White sturgeon (native to Washington, Oregon, California and Idaho in the United States and to the province of British Columbia in Canada) are with biennial ovarian cycles starting in captivity at age 5-7 years. The phases of the ovarian cycle have been determined histologically and through plasma metabolic profiles. Because of the asynchronous onset of vitellogenesis and its long duration, the maturing stocks will typically have three main ovarian stages if sampled in the fall. Currently, measuring the oocyte PI value is the only means to accurately assess ripeness of white sturgeon females. It is better to select females with the PI scores of less than 0.10 for spawning induction, although an egg polarization index of 0.06-0.08 is preferable.
Besides measuring PI values, hormone levels in fish plasma have also been used to determine sturgeon maturity levels. Moberg et al. indicated that plasma concentrations of estradiol-17β (E2) can discriminate the vitellogenic stage in sturgeon. Webb et al. reported that plasma testosterone (T) and E2 were the best predictors to differentiate sex and stages of maturity in white sturgeon. Malekzadeh Viayeh et al. showed that plasma T and E2 plus either age, total length, 272 fork length or weight were also good predictors for the stage of sturgeon sexual maturity. The most viable methods for quantifying sex steroid contents in fish plasma are radioimmunoassay (RIA) and enzyme-linked immunosorbent assay (ELISA). The techniques developed here, provide culturists with models that can be used to predict PI value for the fish from PLSR models developed on the basis of 20,000 FT-IR spectra from a 4-year cumulative study of 543 sturgeon females farmed in both California and Idaho. This method would be appropriate for conservation and restoration efforts in addition to aquaculture been rapidly developed, increasing the field applicability of this technology.
All species of sturgeon are very sensitive to environmental conditions on spawning grounds, such as seasonal temperature, river velocity, and availability of specific spawning substrate. Our spectroscopic method has a potential application for monitoring the stage of the egg maturity and potentially the incidence of pre-ovulatory follicular atresia in natural spawning populations, such as the recently re-established spawning runs of lake sturgeon and the endangered stock of the Kootenai River white sturgeon. These populations, while being protected, may still be at risk due to the changes in the river thermal regime and the high sensitivity of sturgeon spawning to temperature, spawning substrate, and current velocity. A rapid and minimally invasive spectroscopic method such as the ones presented here would provide a unique opportunity for monitoring reproductive health of the brood fish that are on or near natural spawning grounds. It will also permit the determination of the potential impact of pre-ovulatory ovarian follicular atresia, and provide a new tool for predicting the impact of environmental changes on sturgeon populations and may have applications for rapid determination of reproductive status in cobia, barramundi and potentially tunas.
Further Reading
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April 2014
