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Preparation, Characterization, and Cytoprotective Effects on HUVECs of Fourteen Novel Angiotensin-I-Converting Enzyme Inhibitory Peptides From Protein Hydrolysate of Tuna Processing By-Products

To effectively utilize skipjack tuna (Katsuwonus pelamis) processing by-products to prepare peptides with high angiotensin-I-converting enzyme (ACE) inhibitory (ACEi) activity, Neutrase was selected from five kinds of protease for hydrolyzing skipjack tuna dark muscle, and its best hydrolysis conditions were optimized as enzyme dose of 1.6%, pH 6.7, and temperature of 50°C using single factor and response surface experiments. Subsequently, 14 novel ACEi peptides were prepared from the high ACEi protein hydrolysate and identified as TE, AG, MWN, MEKS, VK, MQR, MKKS, VKRT, IPK, YNY, LPRS, FEK, IRR, and WERGE. MWN, MEKS, MKKS, and LPRS displayed significantly ACEi activity with IC50 values of 0.328 ± 0.035, 0.527 ± 0.030, 0.269 ± 0.006, and 0.495 ± 0.024 mg/mL, respectively. Furthermore, LPRS showed the highest increasing ability on nitric oxide (NO) production among four ACEi peptides combining the direct increase and reversing the negative influence of norepinephrine (NE), and MKKS showed the highest ability on directly decreasing and reversing the side effects of NE on the secretion level of endothelin-1 (ET-1) among four ACEi peptides. These findings demonstrate that seafood by-product proteins are potential ACEi peptide sources and prepared ACEi peptides from skipjack tuna dark muscle, which are beneficial components for functional food against hypertension and cardiovascular diseases.


INTRODUCTION
Global fish production reached around 179 million tons, in which marine capture fishery production reached about 84.4 million tons (1). However, ∼50% of these catches are generated as by-products in the manufacturing process (2)(3)(4), and those by-products create burdensome disposal problems and bring serious environmental pollution problems (5)(6)(7). Therefore, researchers keep trying to establish high-value utilization methods for producing useful marketable products using functional components in fish by-products (8)(9)(10)(11)(12). Recently, novel bioprocessing technologies have been developed for utilizing these fish by-product proteins to prepare active peptides for commercial use (3,(13)(14)(15)(16)(17). Tuna and tuna-like species are commercially important marine products worldwide with 7.9 million tons of catches in 2018 (1,18). Among them, skipjack tuna (Katsuwonus pelamis) is the most common and abundant species with 3.2 million tons of catches (1). During the processing of canned tuna, about 50% of skipjack tuna materials are generated as by-products (19)(20)(21). On the contrary, several functional molecules, such as collagen/gelatin, unsaturated fatty acid, protease, and polysaccharides, are generated from skipjack tuna by-products and exhibited huge application prospects and various biological activities (22,23). Moreover, some bioactive peptides have been prepared from protein hydrolysates of tuna by-products, such as bone/frame (24), scale (25), roe (4), milt (21), and head and viscera (26,27). In addition, Chi et al. found that protein hydrolysates of tuna dark muscle presented significant radical scavenging and lipid peroxidation inhibition ability (28). Maeda et al. revealed that the dietary tuna dark muscle could decrease hepatic steatosis and promote serum highdensity lipoprotein cholesterol in obese type-2 diabetic/obese KK-A (y) mice (29).
Hypertension is taken as the most dangerous factor for cardiovascular diseases (CVD) and is closely contacted with multiple complications (30,31). In addition, WHO reported that about 9 million people died from hypertension-related complications and accounted for about 12.8% of the world's total deaths every year, and the proportion of adults with hypertension will increase to 29.2% in 2025 (32). The angiotensin-Iconverting enzyme (ACE) can convert angiotensin I (Ang I) to Ang II for inactivating the vasodilator bradykinin, and ACE inhibitory (ACEi) activity is a noticeable method for mediating systemic hypertension (33,34). Then, chemical synthetic ACE inhibitors, such as captopril, enalapril, and lisinopril, are used as antihypertensive agents in various clinical conditions, but their untoward effects attract sustained and wild concern (31, 35).

Screening of Protease Species
The degreasing process of skipjack tuna dark muscle was performed according to the method described earlier (28). After that, defatted tuna dark muscle powders were dispersed in distilled water (1%, w/v) and hydrolyzed with an enzyme dose of 2% (w/w) using papain (55 • C, pH 7.0), pepsin (37.5 • C, pH 2.0), Alcalase (55 • C, pH 9.5), Neutrase (55 • C, pH 7.0), and trypsin (37.5 • C, pH 7.8), respectively. After 3 h, the enzymolysis reaction was stopped at 95 • C for 20 min and centrifuged at 8,000 × g for a quarter of an hour at −4 • C. The resulting supernatant was lyophilized and stored at −20 • C. The protein hydrolysate produced by Neutrase showed the highest ACEi activity.
According to the single-factor experiment results, response surface methodology was employed to estimate the influence of independent variables (X 1 , pH; X 2 , enzyme dose; and X 3 , temperature) on ACEi activity (47)(48)(49). The three levels (X 1 : 6.5, 7.0, and 7.5; X 2 : 50, 55, and 60 • C; X 3 : 1.0, 1.5, and 2.0%) of the Box-Behnken design were designed to analyze the effects of three variables on ACEi activity. The experimental operation after hydrolysis is the same as the method described earlier.
The protein hydrolysate prepared under the optimal enzymolysis conditions was referred to as TDH.

Separation Process of ACEi Peptides From TDH
ACEi peptides were purified from TDH using the designed process (Figure 1).

Gel Permeation Chromatography
TDH-I solution (5 ml, 50.0 mg/ml) was fractionated with Sephadex G-25 column (3.6 cm × 150 cm) eluted with ultrapure water under 0.6 ml/min flow rate. Each eluate (1.8 ml) was collected by monitoring absorbance at 280 nm. Four subfractions (IG1, IG2, IG3, and IG4) were isolated from the TDH-I solution and lyophilized. The ACEi activity of IG2 was higher than those of the other three fractions.

Identification of Sequence and MW of ACEi Peptide
The sequences of STAP1 to STAP14 were analyzed using an Applied Biosystems 494 protein sequencer (Perkin Elmer, USA) (9). The MWs of STAP1 to STAP14 were determined by employing a quadrupole time-of-flight (Q-TOF) mass spectrometric device (Micromass, Waters, USA) in the combination of an electrospray ionization (ESI) source (50).

Determination of ACEi Activity
The ACEi activity was determined by employing FAPGG as the substrate with the previous method (34). The ACEi activity was calculated directly as the decrease rate in the absorbance at 340 nm in the first 30 min ACE catalyzed hydrolysis of FAPGG. For controlled (uninhibited) reaction, the peptide sample was replaced by HEPES buffer. Half maximal inhibitory concentration (IC 50 ) is the concentration of ACEi peptides required for 50% inhibition of ACE.

HUVEC Culture and Cell Viability Assessment
After thawing, HUVECs were maintained in cultured flasks and cultured to confluence in DMEM containing 1% penicillinstreptomycin liquid, supplemented with 10% FBS. HUVECs received the culturing process at 37 • C in a humidified 5% CO 2 atmosphere (34).
The cytotoxicity of STAP3, STAP4, STAP7, and STAP11 on HUVECs was assessed using MTT assay on manufacturer's instructions (34). In brief, HUVECs received the incubation in 96-well plates at a density of 0.8 × 10 4 cells per well with 180 µl completed DMEM. Following the confluence of 50-60% in the 96-well plates, cells were treated with 20 µl peptides underdesigned concentrations (100, 200, and 400 µM) and further cultured for 24 h at 37 • C. Subsequently, cells were added with 20 µL MTT solutions (5 mg/mL) and incubated for 4 h. Finally, DMSO was added into each well and the absorbance (A) at 490 nm was determined. The cell viability was calculated as: Cell viability % of blank control

Evaluation of NO and ET-1 Production
The NO and ET-1 contents of HUVECs were determined after a 24 h treatment of ACEi peptides (STAP3, STAP4, STAP7, and STAP11, respectively) (34). HUVECs received the plating process in 6-well plates and the treating process with Cap (1 µM), NE (0.5 µM), designed concentrations of ACEi peptides (100, 200, and 400 µM) for 24 h, or treated with both NE (0.5 µM) and 200 µM ACEi peptides for 24 h. NO and ET-1 productions in treated cells were obtained by employing human NO and ET-1 assay kits according to the manufacturers' protocol.

Data Analysis
All data are expressed as the mean ± standard deviation (SD) with triplicate and analyzed using SPSS 20.0 (SPSS Corporation, Chicago, IL, USA). Significant differences were obtained by employing the ANOVA test with Dunnett's or Tukey's test (P < 0.05).

Screening of Protease Species
Protein hydrolysates of skipjack tuna dark muscle were produced by five kinds of proteases. At 1.0 mg/ml, ACEi rate of protein hydrolysate generated by Neutrase was 66.79 ± 1.53%, which was markedly higher than the rates of protein hydrolysates produced using Alcalase (61.43 ± 2.67%), trypsin (43.19 ± 0.70%), pepsin (17.95 ± 0.44%), and papain (38.70 ± 4.65%) (P < 0.05). At present, several methods, including microbial fermentation, solvent extraction, enzymatic hydrolysis, and chemical degradation, have been applied to generate protein hydrolysates, and enzymatic hydrolysis is the most popular and useful process because of its easy-to-control and environmentalfriendly features (3). These biological functions of protein hydrolysates have a close relationship with the composition and structure of bio-peptides, and the protease specificity is the most critical factor in the generation of bioactive protein hydrolysates (39,44,51). Therefore, Alcalase, Neutrase, trypsin, pepsin, papain, and their combinations are continually screened and optimized to apply in the production of protein hydrolysates from various protein resources (51)(52)(53)(54)(55). This study results further supported the conclusion that the specificity of proteases markedly influenced the peptide composition and biological functions of hydrolysates. Therefore, Neutrase was selected to prepare protein hydrolysate of tuna dark muscle.

Optimized the Hydrolysis Conditions of Neutrase Using a Single-Factor Experiment
As shown in Figure 2, the effects of hydrolysis conditions of Neutrase, including pH, enzyme dose, and temperature on the ACEi activity of protein hydrolysates, were optimized by a single-factor experiment. Figure 2A indicated that pH values significantly influenced the ACEi activity of protein hydrolysates, and the ACEi activity (63.54 ± 3.19%) of protein hydrolysates prepared at pH 7.0 was prominently stronger than those of protein hydrolysate prepared at other pH values (P < 0.05). Figure 2B illustrated that the ACEi activity of hydrolysates increased gradually when the temperature ranged from 45 to 55 • C and achieved the highest value (65.33 ± 1.42 %) at 55 • C.
In addition, no significant difference in ACEi activity of protein hydrolysates was observed at 50, 55, and 60 • C (P > 0.05). Figure 2C showed that the ACEi activity of protein hydrolysates was dramatically affected by the enzyme dose, and the ACEi activity (68.64 ± 2.44%) of protein hydrolysate prepared at the dose of 1.5% was prominently stronger than those of hydrolysates prepared at other tested doses (P < 0.05). Additionally, the ACEi activity of protein hydrolysate was slow descent when the dose was higher than 1.5%. Therefore, the range of hydrolysis conditions for Neutrase was narrowed to 9-10, 50-60 • C, and 1.0-2.0% for pH, temperature, and enzyme dose, respectively.

Optimized the Hydrolysis Conditions of Neutrase by Response Surface Experiment
In accordance with the results of single-factor experiment (Figure 2), the range of hydrolysis conditions for Neutrase was optimized for 6.5-7.5, 1.0-2.0%, and 50-60 • C for pH (X 1 ), enzyme dose (X 2 ), and temperature (X 3 ), respectively. Furthermore, the ACEi activity of protein hydrolysates under a response surface experimental design of 3-level, 3-factor factorial is shown in Table 1. After regression fitting of response values and the variables in Table 1 using Design-Expert 8.0.6, the quadratic multinomial regression equation disclosing the relationship between the ACEi rate (Y) and the variables (pH [X 1 ], enzyme dose [X 2 ], and temperature [X 3 ]) was as given below: The results on the significance test of the coefficients of the regression model and variance analysis results of the equation are shown in Table 2, where X 1 , X 2 , X 3 , X 1 X 2 , X 1 X 3 , X 2 X 3 , X 2 1 , and X 2 2 had significant effects on ACEi rate (P < 0.05). The findings made clear that the effects of variances on enzymatic hydrolysis process parameters had an interaction effect, rather than a simple linear relationship. The determination coefficient (R 2 ) of ACEi rate was 0.9673, meaning that 96.73% of the observed results could be fitted well by this regression equation. The relationship between independent variables and the response value could be well described by the regression equation. In addition, the high degree of consistency between the predicted and observed values was proved by the adjusted determination coefficient (RAdj 2 , 0.9523), and the low variation in the mean value and high precision and good test value dependability were manifested by the low coefficient value (C.V.%, 1.49). Furthermore, the model difference was significant (P = 0.0002 < 0.01), and the difference of Lack of Fit (P = 0.6749 > 0.05) was not significant, indicating that the error of the regression equation was small and the model was well fitted. Therefore, the model could be used to analyze and predict the relationship between the ACEi rate of protein hydrolysate and various conditions of Neutrase. In addition, the analysis results in Table 2 indicate that the order of influence of each variance on ACEi rate was enzyme dose (X 2 ) > temperature (X 3 ) > pH (X 1 ). Therefore, the amount of enzyme dose has a great influence on the percentage of ACE inhibition rate in the process of hydrolysis.
The response surface diagram (Figure 3) is drawn according to the multiple nonlinear regression equation. Figure 3 is a 3D response space surface based on the interaction of response values in different experimental conditions and can predict and test the interactive influence of the variables and their mutual interaction on the ACEi rate. As shown in Figure 3A, ACEi rate increased to a certain extent when the pH value increased within a certain range, but ACEi rate decreased instead of increasing when the pH value exceeded this range. The effects of enzyme dosage and temperature showed similar trends. The shape (elliptic or round) of the contour map can reflect the strength and significance of the interaction between two independent variables. Elliptic indicates that the interaction between the two independent variables is evident; round indicates that the interaction between the two factors is not evident. The contour maps of Figures 3A-C are elliptic, indicating that the influence between the two factors (X 1 X 2 , X 1 X 3 , and X 2 X 3 ) is evident. These results agreed well with the data summarized in Table 2. According to the analysis of Design-Expert 8.0.6, the optimal processing conditions of Neutrase for preparing protein hydrolysate of tuna dark muscle were as follows: pH 6.7, enzyme dosage 1.6%, and temperature 55.2 • C. Using the optimum hydrolysis conditions, the ACEi rate of prepared hydrolysate (referred to as TDH) of tuna muscle was 70.96%, which was very close to the predicted 71.28% and confirmed the validity and adequacy of the predicted equation.

Preparation of ACEi Peptides From TDH
Ultrafiltration TDH underwent the fractionation into four different MW peptide fractions (TDH-I, TDH-II, TDH-III, and TDH-IV) by 3.5, 5, and 10 kDa ultrafiltration membranes. The ACEi activity of TDH-I was 51.05 ± 2.14% at 0.5 mg/ml (P < 0.05), which was observably higher than those of TDH (42.47 ± 1.37%), TDH-II (40.20 ± 1.52%), TDH-III (31.70 ± 1.26%), and TDH-IV (30.66 ± 1.37%). Large polypeptides are difficult to enter and bind to the ACE active site, which leads to the reduction of ACEi activity (56). Therefore, ultrafiltration technology often serves as an effective tool to collect functional peptides with low MWs from different protein hydrolysates (3,57). The available findings were consistent with the previous reports that low MW peptide fractions of protein hydrolysates from tuna frame (35), Antarctic krill (34), rice bran (58), cooked chicken breast (56), Cyclina sinensis (59), and soybean (60) had the highest ACEi capabilities. Then, TDH-I with the lowest MW revealed strong ACEi capability and was selected for further separation.

Bioactive Properties of Prepared ACEi Peptides (STAP1-STAP14)
ACEi Activity of Fourteen ACEi Peptides (STAP1-STAP14) In Table 3, IC 50 (half maximal inhibitory concentration) was used to evaluate the inhibitory efficiency of prepared peptides on ACE, and the lower of IC 50 value indicated the more active of peptide. Therefore, STAP7 with IC 50 value of 0.269 ± 0.006 mg/ml showed the most ACEi efficiency among 14 prepared ACEi peptides; other prepared peptides with higher ACEi activity were followed by STAP3 (0.328 ± 0.035 mg/ml), STAP11 (0.495 ± 0.024 mg/ml), and STAP4 (0.527 ± 0.030 mg/ml), respectively. In addition, the IC 50 values of STAP3, STAP4, STAP7, and STAP11 were significantly lower than those of the other 10 ACEi peptides (>0.868 ± 0.110 mg/ml) (P < 0.05). However, there were no statistically significant differences among the four peptides. Additionally, the IC 50 values of STAP3, STAP4, STAP7, and STAP11 were lower than those of ACEi peptides from protein hydrolysates of Saurida elongate (RYRP 3.35 mg/ml) ( (67). The present results demonstrated that STAP3, STAP4, STAP7, and STAP11 had significantly ACEi activity and could serve as hypotensive substances applied in health products. The molecular size determines the affinity of the peptide with ACE active sites because large peptides cannot accommodate the narrow binding channel of ACE (46). Tripeptides of VPP and IPP could easily enter into the ACE channel to bind with Zn 2+ , but larger peptides with 7-11 amino acid residues showed low-interaction scores combining with ACE active site (39). In the experiment, STAP3, STAP4, STAP7, and STAP11 belonged to tripeptide or tetrapeptide, and their small size significantly reduced the difficulty of contact with the binding channel of the ACE active site.
Peptide sequence is another key factor affecting the ACEi ability of antihypertensive peptides. Ser, Leu, and Thr at the C-terminal contribute significantly to improving the ACEi activity of KPLLCS, ELFTT, and KPLL, and this result was also supported by the data in the AHTPDB database that VFPS, MIFPGAGGPEL, PYVRYL, and IRWCT showed strong ACEi ability with IC 50 values of 0.46, 0.03, 1.90, and 1.00 µM (56). Then, Ser residue, especially its hydroxyl group, should play a vital effect on the ACEi capability of STAP4, STAP7, and STAP11 (67). Yust et al. made clear that short peptide sequences with Met residues represent an enormous potential of ACEi peptides (68). Therefore, Met residue at the N-terminus may be one of the key factors of STAP3, STAP4, and STAP11 with high ACEi activity. Additionally, Auwal et al. illustrated that branch aliphatic or dicarboxylic amino acids (Val, Ala, Ile, and Leu) at the N-terminus could positively influence the ACEi ability of peptides (40). So, Leu residue at the N-terminus contributes to the ACEi activity of STAP11.   shown in Figure 6. After incubated for 24 h at 100, 200, and 400 µM, the STAP1 group at 100 µM showed the highest cell viability (108.52 ± 5.19%), but STAP11 at 400 µM presented the lowest cell viability (91.07 ± 1.36%) among four ACEi peptides. Therefore, the cell viability of all peptide groups was higher than 90% of the blank control at the tested concentrations, which indicated that STAP3, STAP4, STAP7, and STAP11 had no significant influence on the proliferation of HUVECs (55). Vascular endothelial cells enshroud the inner surface of blood vessels and act as protective barriers within blood-vessel walls (31,46). In addition, vascular endothelial cells serve as an active source for the metabolism of vasoactive substances and are crucial regulatory factors of vascular tone through generating vasodilatory and vasoconstrictory agents (34,46). Then, HUVEC is an appropriate cell model for illustrating the regulating mechanism of blood pressure. In normal tissues, cells usually maintain a balance between proliferation and apoptosis, and bioactive substances with strong cell proliferation inhibition will break the balance to show their potential toxicity to tissues and are deemed unsuitable to developing non-antitumor health products (34,69). The available results explained that STAP3, STAP4, STAP7, and STAP11 had no significant toxicity to HUVECs and should have the potential in developing antihypertensive products.

Effects of STAP3, STAP4, STAP7, and STAP11 on NO Production
Based on Figure 7 NO can downregulate the synthesis of Ang II type 1 receptor and ACE to antagonize the vascular tone function of angiotensin II, which further takes part in regulating the peripheral and central function of the cardiovascular system and exerts its vasoprotective effect (46). As the most potent vascular endothelium-derived vasodilator, the deficiency of NO will raise the risks of cardiovascular in pathologic situations, and the improvement of endothelial NO production represents reasonable therapeutic strategies for cardiovascular (34,44). Previous studies proved that ACEi peptides, including TYLPVH, FQK, SP, GRVSNCAA, and VDRYF, play their antihypertensive function by increasing the NO generation of HUVECs (64,70). Similarly, KYIPIQ can promote the generation of NO and the expression level of phosphorylated endothelial NO synthase by activating the protein kinase B (Akt) pathway in HUVECs (31). The present finding indicated that STAP3, STAP4, STAP7, and STAP11 could significantly improve the NO production in HUVECs and even reversed the NE-induced downtrend of NO production.
As a known vasoconstriction factor, ET-1 is similar to Ang II and can lead to endothelial dysfunction related to hypertension and atherosclerosis. Previous reports confirmed that GRVSNCAA and TYLPVH from R. philippinarum gave play to their hypotensive activity by prominently reducing ET-1 generation (64), and the umami octapeptide of IPIPATKT could decrease the content of endothelin-1 (ET-1) in insulin-resistant-HepG2 cell and HUVECs models (71). In addition, ADVFNPR, VVLWK, LPILR, and VIEPR presented potent noncompetitive ACEi activity and significantly decreased the ET-1 levels in EA.hy926 cells (72). The available results illustrated that STAP3, STAP4, STAP7, and STAP11 displayed similar capabilities to significantly decrease the secretion of ET-1 and reversed the NE-induced uptrend of ET-1 secretion in HUVECs.

CONCLUSION
The hydrolysis conditions of Neutrase for hydrolyzing the dark muscle protein of skipjack tuna were optimized, and 14 ACEi peptides were isolated from the prepared hydrolysate using Neutrase and identified as TE, AG, MWN, MEKS, VK, MQR, MKKS, VKRT, IPK, YNY, LPRS, FEK, IRR, and WERGE. MWN, MEKS, MKKS, and LPRS displayed noticeable ACEi activity. Moreover, MWN, MEKS, MKKS, and LPRS could exert their protection-related effects upon vascular endothelial functions and display an analogous antihypertensive mechanism as Cap by alleviating the negative effects of NE-constrained NO production and NE-induced ET-1 secretion. Therefore, the mentioned results provide a huge chance for skipjack tuna dark muscle as the materials to generate ACEi peptides, and the prepared ACEi peptides could serve as therapeutical ingredients to control cardiovascular disease. In addition, further study will perform the in vivo experiments for evaluating the therapeutic effects and illustrating the mechanisms of mentioned ACEi peptides to regulate hypertension.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

AUTHOR CONTRIBUTIONS
Q-QQ and Q-BL contributed to data curation, methodology, and formal analysis. S-KS contributed to data curation and formal analysis. Y-QZ contributed to methodology and formal analysis. C-FC contributed to conceptualization, methodology, and supervision. BW contributed to supervision, funding acquisition, writing, reviewing, and editing. All authors contributed to the article and approved the submitted version.