Label-free Quantitative Proteomics Combined With Transcriptomics Revealed the Importance of Cardiac Development in Producing the High Metabolic Capacity of Yellowfin Tuna (Thunnus Albacares)

Tuna are commercially important fish throughout the world, and they are renowned for their endothermy, which allows them to maintain elevated temperatures in the oxidative locomotor muscles, viscera, brain, and eyes while occupying cold, productive, high-latitude waters. The endothermic mechanism is supported by a high heart rate and cardiac output, but the genes and proteins that participate in this cardiac function are poorly known. In this study, we combined label-free quantitative proteomics and transcriptomics to investigate the changes in the heart of yellowfin tuna (Thunnus albacares) before and after they developed endothermy. We identified 515,428 transcripts and 3355 protein groups in the hearts of two development stages of yellowfin tuna. Twenty-eight differentially expressed proteins were correlated with differentially expressed genes. The proteins that accelerate energy production were more highly expressed in the hearts of the large yellowfin tuna compared with the small specimens. Moreover, the proteins in the Z-disk, which protect against mechanical damage, were only detected in the hearts of large fish. These results indicate that as yellowfin tuna grow, the heart develops a self-protection strategy to cope with high metabolic rates and high mechanical forces. The differentially expressed proteins related to cardiac function, which are closely associated with striated muscle differentiation, glycosylation, and cardiac myocytes motility, were highly expressed in the larger (endothermic) tuna than that in the smaller (poikilothermic) tuna. Therefore, we suggest that the heart function of yellowfin tuna changes and improves during the transition from poikilothermic tuna (small size, 126 mm < fork length (FL) < 152 mm, 30 g < body weight < 46 g) to endothermic tuna (large size, 207 mm < FL < 235 mm, 170 g < body weight < 200 g). This is the first report of how gene and protein expression levels explain the strong heart function of yellowfin tuna.

Functional Properties of Yellowfin Tuna (Thunnus albacares) Skin Collagen Hydrolysate Fraction obtained by Ultrafiltration Purification

Hydrolyzed collagen with different fractions is broadly applied in various industries due to its functional properties. The study aimed to purify and fractionate the hydrolyzed collagen from yellowfin tuna skin by ultrafiltration and evaluate the functional properties of its fractions. The effect of temperature, pH, and pressure on membrane flux, nitrogen recovery efficiency, and degree of separation was investigated. Afterward, several functional properties of hydrolyzed collagen fractions including solubility, emulsification, foaming, and antioxidant properties were evaluated. The optimum ultrafiltration conditions for hydrolyzed collagen were temperature 25 °C, pH 6.5 and pressure 12 psi provided optimum membrane flux (3.4 L/m2.h) and nitrogen recovery efficiency (80.81%), and the smallest degree of separation (27.45%). The products after ultrafiltration were separated into two fractions, F1 (< 3 kDa), and F2 (3-5 kDa), with the volume of 10% and 90%, respectively. Both hydrolyzed collagen fractions were more than 96% soluble at pH below 8.0, where the F2 fraction dissolved better than F1. As pH was higher than 8.0, both fractions were almost completely dissolved. In addition, the emulsifying and foaming abilities of the F1 fraction were better than the F2. However, the F2 fraction was more resistant to oxidation with higher antioxidant activity. In conclusion, this research indicates that different fractions from hydrolyzed collagen from yellowfin tuna skin have various functional properties that could be applied in food, cosmetic and pharmaceutical industries.

Changes in microbial composition and quality characteristics of yellowfin tuna under different storage temperature

Yellowfin tuna is one of the commercially important fish varieties, and inappropriate storing may deteriorate its safety and quality. This study aimed to investigate the microbial composition and quality characteristics of yellowfin tuna stored at different temperatures for varying amounts of time. With an increase in the storage temperature and storage time, the biogenic amines, the total volatile basic nitrogen TVB-N, and the total viable cell count steadily increased, which influenced the quality of tuna. The most significant histamine concerning food safety reached levels of 21.25, 235.05, 1166.18, and 3799.29 mg/kg, respectively. The values of total viable cell counts were increased to 7.04, 7.97, 8.24, and 8.91 log CFU/g after storage at 0, 4, 10, and 20 °C for 12 days, 7 days, 7 days, 3 days, respectively. Additionally, changes in microbial composition were evaluated by high-throughput sequencing, and the results showed that Pseudomonas was the dominant spoilage bacteria in yellowfin tuna. The bacterial dynamics and their correlation with biogenic amines and TVB-N in yellowfin tuna were analyzed. A positive correlation between Pseudomonas, Shewanella, Morganella, Acinetobacter, and biogenic amines was found. Pseudomonas showed significant correlation with histamine, cadaverine, and putrescine. This study provides insights into yellowfin tuna quality and microbial composition, which provide theoretical guidance for maintaining seafood safety and quality during distribution and storage.

Identification of yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) using deep learning

Yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) have important economic values for the capture fisheries in Indonesia. Activities of identifying these fish and other types of tuna have been done manually, which can lead to errors and ultimately affect statistics, stock estimates, or traceability. The aim of this research is to use deep learning methods in identifying three species of tuna, specifically yellowfin tuna, mackerel tuna, and skipjack tuna. YOLO’s newest model, YOLOv5, was used to identify the fish. The number of epochs that produces the optimum accuracy value for use in the YOLOv5 model is 400. The values for training loss, accuracy, precision, recall and F1-Score when the model is learning with a total of 400 epochs are 0.000253, 95%, 98.1%, 93.9%, and 96%. Based on these results, the three species of tuna can be identified with high accuracy.

Sample size requirements for genetic studies on yellowfin tuna

In population genetics, the amount of information for an analytical task is governed by the number of individuals sampled and the amount of genetic information measured on each of those individuals. In this work, we assessed the numbers of individual yellowfin tuna (Thunnus albacares) and genetic markers required for ocean-basin scale inferences. We assessed this for three distinct data analysis tasks that are often employed: testing for differences between genetic profiles; stock delineation, and; assignment of individuals to stocks. For all analytical tasks, we used real (not simulated) data from four sampling locations that span the tropical Pacific Ocean. Whilst spatially separated, the genetic differences between the sampling sites were not substantial, a maximum of approximately Fst = 0.02, which is quite typical of large pelagic fish. We repeatedly sub-sampled the data, mimicking a new survey, and performed the analyses. False positive rates were also assessed by re-sampling and randomly assigning fish to groups. Varying the sample sizes indicated that some analytical tasks, namely profile testing, required relatively few individuals per sampling location (n ≳ 10) and single nucleotide polymorphisms (SNPs, m ≳ 256). Stock delineation required more individuals per sampling location (n ≳ 25). Assignment of fish to sampling locations required substantially more individuals, more in fact than we had available (n > 50), although this sample size could be reduced to n ≳ 30 when individual fish were assumed to belong to one of the groups sampled. With these results, designers of molecular ecological surveys for yellowfin tuna, and users of information from them, can assess whether the information content is adequate for the required inferential task.

Application of Surplus Production Model to the Yellowfin Tuna Thunnus albacares in the northern and western parts of Aceh waters

Yellowfin tuna Thunnus albacares is one of pelagic fish that has high potential and economic value in Banda Aceh. Utilization of this resource in Banda Aceh is using purse seine units, with the number of purse seines continuously increasing. Therefore, management needs to be done so that optimal productivity can be maintained. This study discusses the estimation of catch and effort at maximum sustainable yield (MSY) of yellowfin tuna based on catch per unit effort (CPUE) and purse seine production in Banda Aceh during 2013-2018. Mathematical analysis was carried out using the equilibrium approach with the Schaefer model. The highest catch of yellowfin tuna reached 191 tons (July) and the average CPUE for yellowfin tuna was 0.796 tons/trip with CMSY of 2,482 tons/year and EMSY of 2,765 trips/year. From 2015 to 2018, the trend of biomass continued to decline and overfishing occurred during this period.

Small scale tuna fisheries profiles in the Indonesia archipelagic waters

Indonesia is one of the largest tuna producers in the world, which contributes 16% to world tuna production. The dominant tuna species catched in Indonesia are Albacore Tuna (Thunnus alalunga), Madidihang/Yellowfin Tuna (T. albacares), Big Eye Tuna (T. obesus) dan Southern Bluefin Tuna (T. maccoyii). The tuna fisheries have contributed significant jobs or livelihood to the coastal communities. Profit and revenue sharing is a common remuneration system found on tuna fisheries though out Indonesia. However, these fishers are vulnerable given their economic and welfare conditions and of usually limited options of others livelihood. Small fishers have limited access to livelihoods, access to finance and access to skills or fishing technology. Therefore, the inclusion of socio-economic performance or indicators into the tuna fisheries management is crucial in Indonesia.