Impact of Temperature Manipulations on Growth Performance, Body Composition, and Selected Genes of Koi Carp (Cyprinus carpio koi)

Abstract

Aquatic organisms face substantial challenges from climate change, particularly due to rising water temperatures, which significantly impact their growth and survival. This investigation utilized 960 Koi carp (Cyprinus carpio koi) (Initial Body Weight, 0.304 ± 0.005 g). After a 10-day acclimatization period, the fish were distributed equally across 12 glass aquaria (80 × 40 × 45 cm), with three replicates per treatment. This study encompassed two phases. The first phase (10–60 Days Post-Hatching, dph) involved four temperature regimes: T1 (26 °C), T2 (28 °C), T3 (30 °C), and T4 (26/30 °C daily fluctuation). The second phase (60–120 dph) maintained all groups at 30 °C. Initially, T1 exhibited the best growth performance, indicated by the highest Final Body Weight, Weight Gain, Specific Growth Rate (SGR), and Thermal Growth Coefficient (TGC), along with the highest survival rate. Gene expression analysis revealed that HSP70, HSP90, SOD, BCL-2, and FASN were upregulated in T3 and T4, indicative of stress, while MYOD was highest in T1. During the second phase, T4 displayed superior growth and a healthier body composition with elevated moisture and protein, and reduced fat content compared to T1 and T2. HSP70, HSP90, and BCL-2 expression increased significantly in T1, suggesting thermal stress, whereas MYOD levels rose across all treatments, peaking in T4, which correlated with its growth. Further, there were strong relationships among growth parameters, gene expression, and body composition, with T4 exhibiting the highest essential and non-essential amino acids and a unique fatty acid profile. Overall, the results suggest that manipulated temperature significantly influences Koi carp’s characteristics, making it more adaptable to future environmental stress.

1. Introduction

Climate change poses a serious and growing threat to world biodiversity and sustainable development, particularly for aquatic ecosystems [1]. Marine and freshwater environments, including their resident organisms, are particularly vulnerable to climate change, exhibiting greater sensitivity than terrestrial counterparts [2]. This heightened vulnerability is exemplified by the disproportionately high extinction rates projected for aquatic biodiversity compared to terrestrial species under current and future climate change scenarios [3,4]. Furthermore, Ref. [5] found that warming caused a tenfold larger reduction in body size in aquatic ectotherms than in terrestrial ectotherms. Temperature fluctuations are one of the most important variables in climate change, and they are essential to the survival of aquatic species [6].

Temperature variability is a major environmental challenge for aquatic organisms [1], both in natural settings and aquaculture [6], significantly influencing their growth [7], reproductive success [8], and overall survival [8]. This is particularly true for ectotherms like fish, which rely on external temperatures to determine their internal body temperature. As a key environmental driver, temperature directly impacts essential physiological functions, including immune system strength, metabolic activity, and oxygen requirements [9,10]. These temperature effects are critical for maintaining the health and productivity of aquatic species. Temperatures outside the ideal range, particularly extremes of hot and cold, can be harmful and even deadly when exceeding a species’ tolerance limits [11]. For example, during colder months, reduced temperatures can substantially slow the growth of cold-water fish such as salmon [12]. More severe temperature fluctuations can result in widespread mortality [13], impacting both wild fish populations and aquaculture yields.

High temperatures, surpassing an organism’s heat tolerance, intensify metabolic activity and increase the need for oxygen. This heightened oxygen demand coincides with reduced feed intake and immune suppression, creating a synergistic stress response [14]. The challenge is magnified by the decreased oxygen content of warmer water, which further impairs aerobic respiration and overall physiological function [15]. While rapid, short-lived temperature changes may cause performance to optimize at a specific temperature point before decreasing, prolonged exposure to fluctuating temperatures can induce physiological acclimatization [16]. This adaptive response enables organisms to adjust and maintain internal balance by altering underlying physiological processes, such as enzyme function and metabolic rate [17]. Therefore, effective management of temperature variations is critical for both natural aquatic systems and aquaculture operations.

Aquaculture is the fastest-growing food production sector; it provides nearly half of the world’s fish, making its continued expansion vital for meeting the rising demand for animal protein intake [18]. To ensure long-term sustainability, aquaculture must adapt to the challenges posed by climate change, including temperature fluctuations [19]. Fish employ diverse strategies to cope with climate change pressures, particularly temperature fluctuations [19]. Physiologically, fish can evolve in increased heat tolerance through immediate responses, acclimatization processes, and evolutionary adaptations. These adaptations are crucial for maintaining the stability and biodiversity of aquatic ecosystems [20]. A key element of this adaptation involves the regulation of ion transport proteins, such as Na⁺–K⁺-ATPase (NKA), which is crucial for maintaining osmotic balance [21]. In response to temperature shifts, fish can adjust the levels and activity of different NKA isoforms to optimize ion regulation [21]. Additionally, fish can undergo metabolic adjustments, alter membrane fluidity, and modulate protein synthesis, particularly the production of Heat Shock Proteins (HSPs) [22]. HSPs act as molecular chaperones, assisting in the refolding of denatured proteins and maintaining cellular homeostasis under thermal stress [22].

Koi carp (Cyprinus carpio koi) is an exceptionally suitable species for both aquaculture and aquaponics systems due to its remarkable hardiness and adaptability [23]. They are found to flourish in freshwater habitats and have a wide range of thermal tolerance of 18 °C and 24 °C [24]. This adaptability makes them well suited for diverse aquaculture and aquaponics setups, reducing the need for precise temperature control [24]. Their feeding regimen requires adjustments based on seasonal temperature variations, with increased feeding frequency and quantity during warmer months to support accelerated growth [25]. The Koi carp species also demonstrates notable resilience to oxygen fluctuations often associated with temperature changes [26].

The resilience of Koi fish is further exemplified by their ability to employ specific survival strategies during extreme temperature events [27]. During warm seasons, management practices such as increasing shade and aeration become crucial for maintaining sufficient dissolved oxygen levels in the water. Furthermore, the significant genetic diversity within Koi populations contributes to their adaptability [28]. Variations within their genes can confer advantageous traits, including enhanced thermal tolerance and metabolic efficiency, allowing certain individuals to thrive in broader temperature ranges and contribute to the overall population’s resilience against environmental changes, which is a crucial factor for long-term survival and ecosystem health, especially in the face of ongoing climate change pressures [28]. This inherent genetic diversity further reinforces the suitability of Koi for various aquaculture and aquaponics systems. Therefore, this research aims to simulate climate change by manipulating the temperatures affecting Koi fish and monitoring the extent of Koi fish resistance and adaptability to the surrounding environment and the extent of the impact of temperature on growth performance, body composition, and selected genes related to stress response (Heat Shock Protein 70 (HSP70) and Heat Shock Protein 90 (HSP90)), apoptosis (B-cell Lymphoma 2 (BCL-2)), antioxidant activity (Superoxide Dismutase (SOD)), lipid metabolism (Fatty Acid Synthase (FASN)), and muscle development (Myogenic Differentiation 1 (MYOD)).

2. Materials and Methods

2.1. Experimental Design and Setup

The experiment was conducted using Koi fish (Cyprinus carpio koi) embryos collected from the Binhai Research Station of Shanghai Ocean University where the experiment was carried out. A total of 960 embryos were evenly distributed across 12 identical glass tanks (80 × 40 × 45 cm³), with three replicate tanks per treatment. The experiment commenced with 10 dph Koi fish (Initial Body Weight: 0.304 ± 0.005 g; Initial Body Length: 0.111 ± 0.022 cm) spanning a 120-day period (10 to 120 Days Post-Hatching). This study was divided into two phases. In the first phase (10–60 dph), four temperature treatments were applied: T1 (control, constant 26 °C), T2 (constant 28 °C), T3 (constant 30 °C), and T4 (the temperature was adjusted from 26 °C at 6 a.m. to 28 °C at 12 noon and 30 °C at 6 p.m., and the cycle was reversed every day). In the second phase (60–120 dph), all treatments were subjected to a constant temperature of 30 °C. The temperature in all tanks was precisely controlled using heaters (LS SERIES, LS-100 Aquarium Heater, 100 Watts, Adjustable Temperature, Waterproof, Energy Saving, LasenFish, Guangdong, China) (Figure 1).

2.2. Experimental Procedure

Each tank was equipped with adequate aeration for oxygenation and a Shark SH-1000 Multi-Function Filter (Shark, Hangzhou, China). Routine maintenance included bi-weekly filter cleaning to remove waste products. A weekly water exchange was performed, replacing 25% of the aquarium water with fresh, dechlorinated water at the experimental temperature. The controlled photoperiod was maintained, with a 12 h light cycle followed by a 12 h dark period. All experimental groups were fed a consistent diet to satiation 4 to 5 times daily in the first phase and later 3 times daily in the second phase. A commercial feed in 3 different sizes (0.6 mm, 1 mm, and 2 mm) with the nutrient composition of 40% protein, 7% fat, 25% carbohydrates, 3% fiber, 13% ash, and 12% water content was used throughout the experiment.

2.3. Growth Performance and Survival Rate

At the commencement of the experiment, 60 dph, and 120 dph, a sample of fish from each replicate tank was weighed and measured. Initial Body Weight (IBW) and length (IBL) were recorded at the start of each phase, while Final Body Weight (FBW) and length (FBL) were recorded at the end of each phase. A digital balance (PW Balance, ADAM Equipment Co., Oxford, CT, USA) and a digital caliper (Micrometer Calipers with Large LCD Screen, Auto-Off Feature, Inch Millimeter Conversion, 6 Inch Caliper Tools for DIY/Household) were used for these measurements. Also, the number of dead fish during the two phases was recorded. These data were then used to calculate the following growth parameters:

Specific Growth Rate (SGR, %/day) = [(In FBW − In IBW)/number of days] × 100, Weight Gain (WG, g) = FBW − IBW, Daily Weight Gain (DWG, g/Day) = (FBW − IBW)/number of days, Weight Gain (WG, %) = [(FBW − IBW) IBW] × 100, Length Gain (LG, cm) = FBL − IBL, Condition Factor (K) = [FBW/(FBL)3] × 100, Thermal Growth Coefficient (TGC, g/°C/day) = 1000 × [(FBW1/3) − (IBW1/3)]/s[duration (days) × mean temperature ˚C], Geometric Mean Weight (GMW, g) = (FBW × FBL)1/2, death rate (%) = [total number of dead fish at the end of phase (60 or 120 dph)/total number of dead fish at the start of phase (10 dph or 60 dph)] × 100.

2.4. Proximate Analysis of Koi Fish

At the end of the experiment (120 dph experimental period) six fish from each replicate tank were randomly selected for proximate body composition analysis. The proximate analysis included determining moisture content, protein, fat, ash, and gross energy content, following standard analytical methods outlined in [29]. In brief, moisture content was determined by drying samples to a constant weight in an oven at 105 °C. Protein content was quantified using the Kjeldahl method, which involves measuring nitrogen content and multiplying by 6.25. Fat was extracted using a Soxhlet apparatus (Soxtec System HT, 1043 Extraction Unit, Tecator, Höganäs, Sweden). Ash content was determined by combusting the samples at 550 °C in a muffle furnace (SX2-12-10, Rongfeng Corporation, Shanghai, China). Finally, the gross energy content of the carcass samples was measured with an automated bomb calorimeter (Model 1272), a device manufactured by Parr Instruments in Moline, Illinois, USA.

2.5. Amino Acid Analysis

At the end of the experimental period (120 dph), six fish were randomly chosen from each treatment group for amino acid analysis. Tissue samples from each fish were freeze-dried and subsequently homogenized. Amino acid composition was determined by using ion exchange chromatography (IEC) with a Technicon Sequential Multisample (TSM) Amino Acid Analyzer.

2.6. Fatty Acid Analysis

At the end of the experiment (120 dph), the same fish tissues used for amino acid analysis were subjected to fatty acid profile determination according to [30]. In brief, fatty acid methyl esters (FAMEs) were prepared using a 2% sulfuric acid esterification method. Subsequently, gas chromatography analysis was conducted using an Agilent 6890 gas chromatograph equipped with a flame ionization detector and an OMEGAWAX 320 fused silica capillary column (30 m × 0.32 mm internal diameter, 0.25 μm film thickness). The temperature program for the analysis involved a series of temperature ramps, starting at 60 °C and reaching a final temperature of 240 °C. The 37-FAME mix standard served as a reference for identifying the different FAMEs. Fatty acid levels are presented as the percentage of each fatty acid in the total fatty acid composition.

2.7. Real-Time PCR Analysis of Selected Genes

Muscle tissue samples (n = 9/treatment) were collected in liquid nitrogen and stored at −80 °C until analysis. For total RNA extraction, a commercially available kit from AG-Accurate Biotechnology was used, employing a chloroform-free method. An amount of 30 mg of muscle tissue was homogenized in 0.6 mL of RNAex, incubated at room temperature for 2 min, and then centrifuged at 12,000 rpm for 2 min at 4 °C. The supernatant was mixed with 0.6 mL of 70% ethanol and transferred to an RNA mini-column, which was washed with RW1 and RW2 buffers before eluting the RNA with RNase-free water. The RNA concentration and purity were assessed using a NanoDrop spectrophotometer, with samples having A260 nm/A280 nm values between 1.8 and 2.1 and A260 nm/A230 nm values between 2.0 and 2.2 considered of good quality. Genomic DNA was removed from the RNA samples using a gDNA clean reaction mix at 42 °C for 2 min, followed by reverse transcription using the 5× Evo M-MLV RT reaction mix at 37 °C for 15 min, 85 °C for 5 s, and then at 4 °C. The cDNA was diluted three times and stored at −20 °C until RT-qPCR. RT-qPCR was performed using a 2X Syber GreenPro Tag HS premix (Accurate Biotechnology, Hunan, China) with specific primers for six genes: Heat Shock Proteins (HSP70 and HSP90), B-Cell Lymphoma (BCL), Myogenic Differentiation (MYOD), Fatty Acid Synthase (FASN), and Superoxide Dismutase (SOD), with beta-actin as the reference gene. The RT-qPCR reactions involved polymerase inactivation at 95 °C for 20 s, followed by 40 cycles of denaturation and annealing, and a melting stage. Gene expression levels were normalized to the housekeeping gene β-actin (

Table 1. Primers used for qRT-PCR verification of differently expressed genes.

Gene	Gene Bank Code	Sequence (5′-3′)	Annealing Temperature (°C)	Efficiency (%)	Product Length	Refs.
HSP70	XM_042771327.1	F: GTGTCCATCCTGACCATTGAAGA	60	97.97	79	[32]
		R: CTGACTGATGTCCTTCTTGTGCTTC
HSP90	XM_042778180.1	F: TCCGTGGTGTGGACTCTG	60	97.89	71	[33]
		R: TCCAGGCACTTCTTGACGATGTTC
BCL-2	XM_042759195.1	F: GGCGTAAGGGATAGGTCAACA	62	98.01	151	[34]
		R: GGTCCCGAGCAGTTCAGAAA
SOD	XM_042761153.1	F: GATGGCAGCCTTGGAAGTGAC	60	97.91	89	[35]
		R: TCAGAACAATCAGGAAGGAGGAA
FASN	KY378913.1	F: TGTATGCCACCGCTTATTATTCC	60	97.9	146	[36]
		R: TCCTTTGCCCTGAGTGTTGA
MYOD	XM_019068329.2	F: ATGGAGTTGTCGGATATTCCCTTC	61	97.88	100	[37]
		R: GCGGTCAGCGTTGGTTGTT
β-actin	XM_019089433.2	F: CCGTAAGGACCTGTATGCCAAC	61	98.12	78	[32]
		R: GACAGAGTATTTACGCTCAGGTGG

Gene abbreviations are HSP70 (Heat Shock Protein 70), HSP90 (Heat Shock Protein 90), BCL-2 (B-cell Lymphoma 2), SOD (Superoxide Dismutase), FASN (Fatty Acid Synthase), MYOD (Myogenic Differentiation 1), and β-actin (housekeeping gene). “F” refers to the forward primer, “R” represents the reverse primer, and “Refs.” is References.

) and analyzed using the 2^−ΔΔct method expressed as fold changes as described in [31].

2.8. Statistical Analysis

Data normality was verified using the Kolmogorov–Smirnov test, while Levene’s test assessed the homogeneity of variances. One-way ANOVA was utilized to detect significant differences in growth performance, body composition, amino acid profiles, and fatty acid profiles across the four temperature treatments. Post hoc Tukey’s multiple comparison tests were performed at the 0.05 significance level to identify specific group differences. All statistical analyses were conducted using GraphPad Prism^® 9 software. Furthermore, the correlations between different data sets were investigated using the “corrplot” package in R software (version 4.4.1).

3. Results

3.1. Growth Performance and Survival Rate in the First Phase

Table 2. Growth performance of Koi carp (means ± SEM) exposed to different temperatures (60 Days Post-Hatching, first phase).

Parameter	Treatment				p-Value
	T1	T2	T3	T4
IBW (g)	0.3100 ± 0.006	0.3033 ± 0.009	0.3000 ± 0.000	0.3033 ± 0.007	0.7227
FBW (g)	29.61 ± 0.504 a	28.69 ± 0.192 a	26.17 ± 0.279 b	24.60 ± 0.867 b	0.0005
WG (g)	29.30 ± 0.506 a	28.38 ± 0.183 a	25.87 ± 0.279 b	24.29 ± 0.867 b	0.0005
DWG (g)	0.586 ± 0.010 a	0.568 ± 0.004 a	0.517 ± 0.006 b	0.486 ± 0.017 b	0.0005
WG (%)	9461 ± 271.5 a	9369 ± 213.2 a	8622 ± 92.90 ab	8016 ± 325.9 b	0.0089
SGR (%/day)	9.119 ± 0.056 a	9.100 ± 0.045 a	8.937 ± 0.021 ab	8.790 ± 0.082 b	0.0095
GMW (g)	3.029 ± 0.034 a	2.949 ± 0.052 ab	2.802 ± 0.015 bc	2.730 ± 0.058 c	0.0046
TGC (g/°C⋅Day)	1.859 ± 0.015 a	1.707 ± 0.001 b	1.533 ± 0.007 c	1.597 ± 0.025 c	0.0001
IBL (cm)	0.110 ± 0.006	0.108 ± 0.006	0.117 ± 0.033	0.110 ± 0.044	0.9960
FBL (cm)	10.95 ± 0.432 a	9.163 ± 0.120 b	8.800 ± 0.083 bc	8.050 ± 0.119 c	0.0001
LG (cm)	10.84 ± 0.427 a	9.056 ± 0.119 b	8.683 ± 0.104 bc	7.940 ± 0.090 c	0.0001
K	2.289 ± 0.236 c	3.735 ± 0.129 b	3.843 ± 0.110 b	4.713 ± 0.058 a	0.0001

The abbreviations for the key parameters are as follows: IBW (Initial Body Weight), FBW (Final Body Weight), WG (Weight Gain), DWG (Daily Weight Gain), SGR (Specific Growth Rate), GMW (Geometric Mean Weight), TGC (Thermal Growth Coefficient), IBL (Initial Body Length), FBL (Final Body Length), LG (Length Gain), and K (Condition Factor). Means within the same row with no common superscripts are significantly different (p < 0.05).

demonstrates that the initial weights and lengths of the treatments were statistically similar across all treatment groups. Growth parameters under T1 exhibited the highest Final Body Weight and Weight Gain, differing significantly (p = 0.0005) from T3 and T4. While T1 and T2 showed similar Daily Weight Gain values, they were significantly higher (p = 0.0005) than those observed in T3 and T4. T1 also had the highest Specific Growth Rate and Geometric Mean Weight, with statistically significant differences (p < 0.05) observed between the treatments. The Thermal Growth Coefficient was highest for T1 and differed significantly (p = 0.0001) from the other treatments. Final Body Lengths were highest in T1, significantly exceeding (p = 0.0001) those of T3 and T4. Length Gain followed a similar pattern to the Final Body Length, with T1 showing the highest increase. The Condition Factor (K) value was significantly lower (closer to 1) in T1 compared to all other treatments (p = 0.0001). Survival rates, as illustrated in Figure 2, showed a decreasing trend across treatments. T1 exhibited the highest survival rates, ranging from 80% to 100%. T2 and T3 demonstrated intermediate survival rates, while T4 consistently exhibited the lowest survival rates among all treatments.

3.2. Growth Performance and Survival Rate in the Second Phase

As shown in

Table 3. Growth performance of Koi carp (means ± SEM) exposed to stress temperatures (120 Days Post-Hatching, second phase).

Parameter	Treatment				p-Value
	T1	T2	T3	T4
IBW (g)	29.61 ± 0.504 a	28.69 ± 0.192 a	26.17 ± 0.279 b	24.60 ± 0.867 b	0.0005
FBW(g)	75.43 ± 2.875 c	83.46 ± 0.922 bc	91.80 ± 2.530 b	103.3 ± 2.053 a	0.0001
WG(g)	45.82 ± 2.421 d	54.78 ± 0.777 c	65.63 ± 2.253 b	78.67 ± 1.225 a	0.0001
DWG (g)	0.764 ± 0.040 d	0.913 ± 0.013 c	1.094 ± 0.038 b	1.311 ± 0.020 a	0.0001
WG (%)	154.6 ± 5.938 d	190.9 ± 2.038 c	250.7 ± 6.023 b	320.3 ± 7.075 a	0.0001
SGR(%/day)	1.556 ± 0.038 d	1.780 ± 0.012 c	2.091 ± 0.029 b	2.393 ± 0.028 a	0.0001
GMW(g)	47.26 ± 1.278	48.93 ± 0.413	49.01 ± 0.937	50.39 ± 1.381	0.3025
TGC (g/°C⋅Day)	0.628 ± 0.021 c	0.727 ± 0.006 c	0.856 ± 0.017 b	0.991 ± 0.005 a	0.0005
IBL (cm)	10.95 ± 0.432 a	9.163 ± 0.120 b	8.800 ± 0.083 bc	8.050 ± 0.119 c	0.0001
FBL (cm)	18.40 ± 0.557 b	20.29 ± 0.300 ab	20.87 ± 0.384 a	22.29 ± 0.643 a	0.0035
LG (cm)	7.447 ± 0.494 c	11.12 ± 0.241 b	12.07 ± 0.367 b	14.24 ± 0.575 a	0.0001
K	1.217 ± 0.076	1.003 ± 0.051	1.011 ± 0.030	0.9403 ± 0.073	0.0526

The following abbreviations for key parameters are IBW (Initial Body Weight), FBW (Final Body Weight), WG (Weight Gain), DWG (Daily Weight Gain), SGR (Specific Growth Rate), GMW (Geometric Mean Weight), TGC (Thermal Growth Coefficient), IBL (Initial Body Length), FBL (Final Body Length), LG (Length Gain), and K (Condition Factor). Means within the same row with no common superscripts are significantly different (p < 0.05).

, T4 exhibited significantly higher Final Body Weight, Weight Gain, Daily Weight Gain, Weight Gain Percentage, and Specific Growth Rate compared to the other treatments (p < 0.0001). While T4 showed the highest Thermal Growth Coefficient (p = 0.0005), there were no significant differences in Geometric Mean Weight between treatments (p = 0.3025). The initial length is the Final Body Length of the first phase. Final Body Length and Length Gain were significantly highest in T4 (p < 0.05), whereas the Condition Factor showed no significant differences among the treatments. Figure 3 depicts the survival rate among all treatments, T4 exhibited the highest survival rate. Statistically significant differences (p < 0.05) were observed between T4 and T1, where T1 demonstrated the lowest survival rate, as indicated by the asterisk.

3.3. Proximate Analysis of Fish in the Second Phase

There were significant differences in moisture, protein, fat, ash, and carbohydrate contents, and gross energy (p < 0.05) among treatments. Comparing T4 to T1 and T2, the moisture and protein content was significantly higher (p < 0.05), with T3 displaying an intermediate value. T3 and T4 had significantly lower fat content than T1 and T2 (p < 0.05). While T1 had a significantly lower ash content, T2, T3, and T4 did not differ significantly from one another. From T1 to T4, the percentage of carbohydrates decreased, and the difference between T1 and T3 is significant (p < 0.05). Gross energy levels were significantly higher (p < 0.05) in T4 compared to T1, with a gradual decrease observed across the treatment groups as shown in

Table 4. Proximate composition of Koi carp (means ± SEM) exposed to stress temperatures (120 Days Post-Hatching, second phase).

Parameters	Treatments				p-Value
	T1	T2	T3	T4
Moisture%	73.59 ± 0.377 a	73.42 ± 0.207 a	72.36 ± 0.176 ab	71.14 ± 0.609 b	0.0067
protein (%DM)	71.63 ± 1.335 b	74.27 ± 0.997 b	78.64 ± 0.678 a	81.72 ± 0.532 a	0.0003
Fat (%DM)	13.53 ± 0.692 a	13.04 ± 0.243 a	10.70 ± 0.350 b	10.47 ± 0.287 b	0.0017
Ash (%DM)	6.196 ± 0.084 b	7.235 ± 0.072 a	7.493 ± 0.107 a	7.221 ± 0.106 a	0.0001
Carbohydrate (%DM)	7.668 ± 2.530	5.558 ± 1.518	2.176 ± 0.521	1.789 ± 0.254	0.0694
Gross energy (kcal−1⋅100 g−1)	123.4 ± 1.276 d	130.8 ± 0.546 c	142.9 ± 1.552 b	148.6 ± 0.579 a	0.0001

Means within the same row with no common superscripts are significantly different (p < 0.05).

.

3.4. Expression of Selected Genes in the First Phase

Figure 4 presents the relative mRNA expression levels of six genes in Koi carp exposed to different temperatures. HSP70 expression showed a significant upregulation in T3 and T4 compared to T1 and T2 (p < 0.05). HSP90 expression was significantly upregulated (p < 0.05) in T3 and T4 compared to T1, with T2 showing intermediate expression. BCL-2 expression was significantly upregulated (p < 0.05) across treatments, with upregulated expression in T4. MYOD expression was significantly upregulated (p < 0.05) in T1 compared to all other treatments. Finally, FASN and SOD expression upregulated significantly (p < 0.05) in T3 and T4 compared to T1 and T2.

3.5. Expression of Selected Genes in the Second Phase

Figure 5 presents the relative mRNA expression levels of six genes in Koi carp exposed to different temperatures at 120 Days Post-Hatching (dph). HSP70HSP70 expression was significantly upregulated (p < 0.05) in T1 compared to T3 and T4. HSP90 expression showed a similar pattern, with significantly upregulated (p < 0.05) expression in T1 and T2 compared to T3. BCL-2 expression was significantly upregulated (p < 0.05) in T1 compared to T4 and T3. MYOD expression was progressively upregulated (p < 0.05) across treatments, with the highest expression in T4, significantly differing from T1 and T2. SOD expression levels did not show any statistically significant differences (p > 0.05) between treatment groups. Lastly, FASN levels were upregulated in T1, and then decreased gradually but not significantly (p > 0.05) across treatments T2, T3, and T4.

3.6. Relationships Between Gene Expression and Other Parameters

3.6.1. Relationships Among the Expression of Selected Genes Between the First and Second Phase

Figure 6 presents a correlation matrix illustrating the relationships between gene expression levels at two phases. At 60 dph, strong positive correlations were observed among the HSP70, HSP90, SOD, BCL-2, and FASN gene expression levels (r = 0.94 to 1.00), while MYOD expression was negatively correlated with all other genes (r = −0.82 to −0.90). At 120 dph, HSP70, HSP90, and BCL-2 remained strongly positively correlated (r = 0.98 to 1.00). SOD showed moderate positive correlations with HSP70 and HSP90 (r = 0.81 and 0.83, respectively) and a weaker correlation with BCL-2 (r = 0.80). FASN exhibited a negative correlation with MYOD (r = −0.97) and positive correlations with HSP70 and HSP90 (r = 0.86 and 0.94, respectively).

3.6.2. Relationships Between Growth Parameter and Gene Expression in the First Phase

The correlations between growth parameters and gene expression appear more nuanced, with a mix of positive, negative, and weak correlations. Figure 7 presents a correlation analysis between growth performance parameters (SGR, WG, GMW, and TGC) and the expression of several genes (HSP70, HSP90, BCL-2, SOD, FASN, and MYOD) in Koi carp exposed to different temperatures in the first phase (60 dph). A strong positive correlation is observed between the growth parameters (SGR, WG, GMW, and TGC), as indicated by the dark blue color, and the opening of the pie shape related to Pearson r (r = 0.74 to 0.94). HSP70 and HSP90 show a strong positive correlation with each other (r = 0.72). SGR displays a moderate negative correlation with FASN (r = −0.86) and a weak positive correlation with MYOD (r = 0.63). WG and GMW show a strong negative correlation with FASN (r = −0.94, −0.89, respectively). SOD exhibits a moderate positive correlation with FSAN and a negative correlation with MYOD (r = 0.88, −0.67, respectively).

3.6.3. Relationships Between Growth Parameter and Gene Expression in the Second Phase

Figure 8 presents a correlation matrix illustrating the relationships between growth performance parameters and gene expression levels in Koi carp exposed to manipulated temperatures at (120 dph). Positive correlations (r = 0.58 to 1.00) were observed among Specific Growth Rate (SGR), Weight Gain (WG), and Geometric Mean Weight (GMW). These growth parameters exhibited negative correlations with the expression of Heat Shock Protein genes HSP70 and HSP90 (r = −0.25 to −0.90). A negative correlation (r = −0.40) was observed between the Thermal Growth Coefficient (TGC) and the expression of the SOD gene. BCL-2 gene expression showed positive correlations with HSP70 and HSP90 (r = 0.78 and r = 0.79, respectively) and negative correlations with growth performance parameters. MYOD expression was positively correlated with growth while showing a negative correlation (r = −0.10) with FASN gene expression. Lastly, FASN expression showed a negative correlation with SGR (r = −0.45) and WG (r = −0.41).

3.6.4. Relationships Between Body Composition and Gene Expression in the Second Phase

Figure 9 depicts a correlation matrix illustrating the relationships between body composition parameters and the expression levels of key genes in Koi carp following exposure to manipulated temperatures at the second phase (120 dph). Protein exhibited a strong positive correlation with gross energy (r = 0.93) and MYOD expression (r = 0.84), and a strong negative correlation with fat (r = −0.83), HSP70 (r = −0.81), HSP90 (r = −0.90), and BCL-2 (r = −0.83). Fat showed a strong positive correlation with HSP70 (r = 0.81), HSP90 (r = 0.90), and BCL-2 (r = 0.90), and a strong negative correlation with protein (r = −0.83), gross energy (r = −0.84), and MYOD (r = −0.76). Ash demonstrated moderate positive correlations with protein (r = 0.69) and gross energy (r = 0.74) and a moderate negative correlation with fat (r = −0.67). Gross energy correlated positively with protein (r = 0.93), ash (r = 0.74), and MYOD (r = 0.83), and negatively with fat (r = −0.84), HSP70 (r = −0.87), HSP90 (r = −0.90), and BCL-2 (r = −0.80). HSP70 and HSP90 exhibited a strong positive correlation with each other (r = 0.86), fat, and BCL-2. BCL-2 correlated strongly with fat (r = 0.90) and moderately with HSP70 (r = 0.78) and HSP90 (r = 0.79). SOD displayed a moderate positive correlation with FASN (r = 0.58).

3.7. Amino Acid Analysis in the Second Phase

Table 5. Amino acid profiles of Koi carp (means ± SEM) exposed to stress temperatures (120 Days Post-Hatching, second phase).

Amino Acid (g/100 g)	Treatments				p-Value
	T1	T2	T3	T4
Essential
Threonine	3.237 ± 0.013	3.322 ± 0.027	3.331 ± 0.011	3.337 ± 0.045	0.1011
Valine	3.211 ± 0.007 b	3.260 ± 0.037 b	3.291 ± 0.026 b	3.517 ± 0.028 a	0.0002
Methionine	2.038 ± 0.010 b	2.060 ± 0.007 ab	2.106 ± 0.021 ab	2.125 ± 0.021 a	0.0162
Isoleucine	3.062 ± 0.052 b	3.071 ± 0.023 b	3.096 ± 0.041 b	3.345 ± 0.038 a	0.0028
Leucine	5.547 ± 0.050 b	5.622 ± 0.023 b	5.628 ± 0.039 b	5.807 ± 0.031 a	0.0067
Phenylalanine	3.404 ± 0.048 b	3.481 ± 0.067 ab	3.442 ± 0.020 b	3.653 ± 0.029 a	0.0190
Lysine	6.900 ± 0.030 bc	6.995 ± 0.018 ac	6.849 ± 0.021 b	7.082 ± 0.015 a	0.0003
Histidine	2.639 ± 0.041 c	2.800 ± 0.027 b	2.792 ± 0.022 b	2.948 ± 0.015 a	0.0004
Arginine	4.955 ± 0.073 b	5.088 ± 0.030 b	4.994 ± 0.068 b	5.496 ± 0.061 a	0.0008
Total essential amino acid	34.991 ± 0.209 c	35.699 ± 0.118 b	35.528 ± 0.147 bc	37.311 ± 0.036 a	0.0001
Non-essential
Aspartic acid	7.539 ± 0.043 c	7.722 ± 0.030 b	7.729 ± 0.028 b	7.989 ± 0.039 a	0.0002
Serine	2.942 ± 0.003 ab	3.032 ± 0.019 ab	3.032 ± 0.003 ab	2.914 ± 0.080 a	0.1659
Glutamic acid	11.136 ± 0.035 c	11.453 ± 0.034 b	11.528 ± 0.016 b	11.714 ± 0.071 a	0.0001
Glycine	3.888 ± 0.013 b	3. 853 ± 0.031 b	4.064 ± 0.058 ab	4.163 ± 0.069 a	0.0056
Alanine	4.358 ± 0.012 c	4.432 ± 0.030 bc	4.563 ± 0.008 ab	4.637 ± 0.053 a	0.0009
Tyrosine	3.264 ± 0.064	3.304 ± 0.041	3.237 ± 0.066	3.388 ± 0.033	0.2808
Proline	2.382 ± 0.043 b	2.626 ± 0.048 b	2.655 ± 0.031 b	2.658 ± 0.029 a	0.0025
Cysteine	0.443 ± 0.012 c	0.440 ± 0.008 c	0.398 ± 0.006 b	0.502 ± 0.007 a	0.0002
Total non-essential amino acid	35.953 ± 0.195 c	36.676 ± 0.253 bc	37.207 ± 0.065 ab	37.965 ± 0.270 a	0.0009
Total amino acid	70.94 ± 0.390 c	72.38 ± 0.370 bc	72.74 ± 0.210 b	75.28 ± 0.304 a	0.0001

Means within the same row with no common superscripts are significantly different (p < 0.05).

presents the amino acid profiles of Koi carp exposed to four manipulated temperature treatments (T1-T4) at 120 Days Post-Hatching (dph). Significant differences (p < 0.05) were observed in the levels of both essential and non-essential amino acids across the temperature groups. Specifically, levels of threonine, valine, methionine, isoleucine, leucine, phenylalanine, lysine, and arginine varied significantly (p < 0.05) between treatments. Similarly, significant differences (p < 0.05) were noted in serine, aspartic acid, glutamic acid, glycine, alanine, tyrosine, proline, and cystine levels across the temperature groups. Total essential amino acid content was highest in T4 and lowest in T1, while total non-essential amino acids were highest in T4 and lowest in T3. Consequently, the total amino acid content was significantly higher (p < 0.05) in T4 (52.28 ± 0.30 g/100 g) compared to all other treatment groups.

3.8. Fatty Acid Analysis in the Second Phase

This study investigated the impact of manipulated temperatures on the fatty acid composition of Koi carp at (120 dph). The results, summarized in

Table 6. Fatty acid composition of Koi carp (means ± SEM) exposed to stress temperatures (120 Days Post-Hatching, second phase).

Fatty Acid (%)	Treatment				p-Value
	T1	T2	T3	T4
C14:0	0.822 ± 0.011 b	0.896 ± 0.004 a	0.839 ± 0.003 b	0.673 ± 0.003 c	0.0001
C15:0	0.161 ± 0.006 b	0.164 ± 0.001 b	0.185 ± 0.001 a	0.152 ± 0.001 b	0.0003
C15:1n5	0.0157 ± 0.0004 b	0.0155 ± 0.0003 b	0.021 ± 0.0001 a	0.0148 ± 0.0005 b	0.0001
C16:0	14.548 ± 0.607 b	15.106 ± 0.020 b	15.933 ± 0.331 ab	17.129 ± 0.142 a	0.0043
C16:1n7	8.812 ± 0.420 a	8.037 ± 0.080 a	6.283 ± 0.288 b	4.888 ± 0.140 c	0.0001
C17:0	0.325 ± 0.005 ab	0.303 ± 0.003 b	0.345 ± 0.012 a	0.188 ± 0.007 c	0.0001
C17:1n7	0.310 ± 0.005 c	0. 302 ± 0. 002 c	0.298 ± 0.013 bc	0.268 ± 0.002 ab	0.0171
C18:0	5.856 ± 0.008 b	5.834 ± 0.014 b	6.694 ± 0.015 a	6.749 ± 0.019 a	0.0001
C18:1n9c	38.042 ± 0.072 a	37.811 ± 0.100 a	36.370 ± 0.085 b	37.781 ± 0.116 a	0.0001
C18:2n6c	22.780 ± 0.148 a	22.561 ± 0.189 a	22.733 ± 0.083 a	21.481 ± 0.095 b	0.0004
C20:0	0.255 ± 0.003 ab	0.223 ± 0.010 b	0.271 ± 0.012 a	0.233 ± 0.003 b	0.0126
C18:3n6	0.263 ± 0.004 d	0.324 ± 0.008 c	0.612 ± 0.012 a	0.494 ± 0.014 b	0.0001
C20:1	1.673 ± 0.012 b	1.658 ± 0.022 b	1.827 ± 0.023 a	1.801 ± 0.014 a	0.0003
C18:3n3	0.576 ± 0.022 c	0.662 ± 0.008 c	0.836 ± 0.011 b	0.964 ± 0.045 a	0.0001
C20:2	0.634 ± 0.005 c	0.642 ± 0.007 c	0.733 ± 0.006 a	0.702 ± 0.005 b	0.0001
C22:0	0.206 ± 0.001 c	0.213 ± 0.002 bc	0.240 ± 0.003 a	0.225 ± 0.005 b	0.0002
C20:3n6	1.210 ± 0.006 d	1.364 ± 0.007 c	1.465 ± 0.003 b	1.553 ± 0.017 a	0.0001
C22:1n9	0.070 ± 0.001 b	0.073 ± 0.001 bc	0.084 ± 0.001 a	0.079 ± 0.002 ac	0.0002
C20:3n3	0.126 ± 0.006 b	0.134 ± 0.002 bc	0.159 ± 0.002 a	0.146 ± 0.005 ac	0.0021
C20:4n6	1.032 ± 0.008 c	1.067 ± 0.006 c	1.242 ± 0.006 b	1.355 ± 0.029 a	0.0001
C23:0	0.049 ± 0.002 bc	0.044 ± 0.00 c	0.055 ± 0.001 ab	0.062 ± 0.003 a	0.0003
C22:2n6	0.131 ± 0.004 c	0.147 ± 0.001 c	0.179 ± 0.002 b	0.205 ± 0.005 a	0.0001
C20:5n3 (EPA)	0.441 ± 0.001 c	0.471 ± 0.002 b	0.489 ± 0.002 a	0.469 ± 0.006 b	0.0001
C24:1n9	0.140 ± 0.002 b	0.159 ± 0.004 ab	0.176 ± 0.001 a	0.166 ± 0.008 a	0.0032
C22:6n3 (DHA)	1.522 ± 0.016 d	1.790 ± 0.022 c	1.929 ± 0.021 b	2.130 ± 0.051 a	0.0001
ΣSFAs	22.238 ± 0.616 b	22.798 ± 0.003 b	24.583 ± 0.302 a	25.352 ± 0.072 a	0.0006
ΣMUFAs	51.262 ± 2.096 a	47.738 ± 0.187 ab	44.741 ± 0.363 b	44.715 ± 0.206 b	0.0075
ΣPUFAs	28.715 ± 0.130 c	29.161 ± 0.185 bc	30.379 ± 0.076 a	29.499 ± 0.108 b	0.0001
Σn-3 PUFAs	2.665 ± 0.013 d	3.057 ± 0.013 c	3.414 ± 0.012 b	3.709 ± 0.011 a	0.0001
Σn-6 PUFAs	25.416 ± 0. 141 b	25.462 ± 0.190 b	26.231 ± 0.088 a	25.088 ± 0.060 b	0.0016
n-3/n-6	0.105 ± 0.001 d	0.120 ± 0.001 c	0.130 ± 0.001 b	0.148 ± 0.0003 a	0.0001

PUFAs (polyunsaturated fatty acids), SFAs (saturated fatty acids), DHA (docosahexaenoic acid), EPA (Eicosapentaenoic Acid), and MUFAs (monounsaturated fatty acids). Means within the same row with no common superscripts are significantly different (p < 0.05).

, demonstrate significant differences (p < 0.0001) in the fatty acid profiles across the four temperature treatments (T1, T2, T3, and T4). Specifically, the levels of myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1n9c), linoleic acid (C18:2n6c), linolenic acid (C18:3n3), arachidonic acid (C20:4n6), and docosahexaenoic acid (C22:6n3) varied significantly among treatments. Treatment T4 exhibited the highest concentrations of myristic acid, palmitic acid, and oleic acid. Stearic acid (C18:0) levels were highest in Treatment T3, while Treatment T1 showed the highest levels of linoleic acid (C18:2n6c) (Table 6).

4. Discussion

Elevated temperatures, even those fluctuating, negatively impact the early developmental stages of Koi fish, hindering their growth and survival. However, exposure to these higher temperatures during early life, while initially detrimental, can act as an acclimatization period. Despite the initial challenges, this thermal conditioning ultimately enhances the performance and resilience of Koi fish in later life stages when confronted with similarly high temperatures. Thus, early thermal stress may be necessary to adapt to future warm conditions.

The growth performance and death rate of Koi carp exposed to different temperatures across two time phases were analyzed. In the first phase (60 dph), the results indicate significant differences (p < 0.0005) between temperature treatments for Final Body Weight, Weight Gain, Daily Weight Gain, Specific Growth Rate, and Geometric Mean Weight. The highest growth performance was generally observed in treatments under 26 °C (T1), and the lowest growth performance was shown in the treatments under 30 °C and 26/30 °C (T3 and T4). Moreover, a high death rate was shown in T4 followed by T3 more than in other treatments. Consistently, the findings of this study demonstrate that climate change, with a primary emphasis on temperature variables, significantly negatively impacts fish growth, manifesting in alterations to fish physiology and overall health at both global and local scales [7]. Furthermore, elevated temperatures can negatively impact growth performance and decrease survival rates, as demonstrated in juvenile Greenland halibut, where optimal growth conditions are typically exceeding 7.5 °C [38]. An increase in temperature substantially elevates their metabolic rate, frequently exceeding the energy acquired from food consumption [39]. This elucidates the significant involvement of temperature manipulation in growth and survival, emphasizing its crucial role in maintaining an optimal growth rate.

In contrast, in the second phase (60–120 dph), when all treatments were under the same stress temperature of 30 °C, the growth performance showed higher FBW, WG, DWG, WG%, SGR, TGC, and FBL in T4 followed by T3 more than in other treatments. Survival rates were significantly lower in T1 compared to T4. Fish reared under oscillating temperatures (T4) exhibited the lowest growth performance in the first phase but demonstrated the highest performance in the second phase. This may be attributable to oscillating temperatures that might better mimic the natural environment of some fish species, particularly those that experience diurnal or seasonal temperature variations. While the initial adjustment to these fluctuations might hinder growth, the fish might be better adapted to thrive under such conditions in the long term, leading to superior performance in the second phase compared to fish held at a constant temperature. Furthermore, the ability of fish to modify their metabolic rates in response to temperature changes is called thermal acclimatization [40]. Our findings align with Nairoby’s research, which indicates that fluctuating temperatures may confer physiological benefits to longfin yellowtail (Seriola rivoliana) embryos. This suggests that S. rivoliana possesses a high degree of adaptability to temperature variations during embryonic development [39]. Moreover, Lake Whitefish embryos exhibit altered metabolism and growth patterns in response to daily fluctuations in incubation temperature [41]. Acclimatization is a physiological process that allows organisms to adapt to changing environmental conditions. This involves a suite of physiological adjustments, such as changes in enzyme activity, metabolic rate, and gene expression [42]. These adaptations enable organisms to maintain homeostasis and function optimally within a specific temperature range. In fish, this can include the expression of temperature-specific enzyme isoforms, modifications to cell membranes, and increased production of red blood cells and Heat Shock Proteins to ensure proper function in varying temperatures [42]. From these results, Koi carp showed the ability to adapt to high temperatures and have high growth and survival rates.

The second phase of this study revealed significant differences in proximate composition among treatments. Notably, T4 exhibited elevated moisture and protein levels, while T3 and T4 displayed reduced fat content. A significant decrease in carbohydrate percentage was observed from T1 to T4, with a notable difference between T1 and T3. Furthermore, T4 demonstrated significantly higher gross energy levels compared to T1. Our results align with a study on Labeo rohita, in which growth performance and proximate analysis were positively correlated with increasing water temperatures (25 °C, 30 °C, and 35 °C) [43]. Moreover, the proximate composition, including moisture, protein, fat, and ash contents, exhibited minor fluctuations across the different temperature and dietary protein treatments [43,44]. The findings of another study were different, revealing that early-life thermal stress in rainbow trout can impair future physiological function without necessarily impacting growth or proximate body composition. Temperature exerts a profound influence on various physiological processes in Koi fish, including metabolism, growth, and nutrient utilization [45]. Elevated temperatures typically accelerate metabolic rates, leading to faster growth and potential shifts in the composition of muscle and fat stores [46]. Furthermore, the efficiency of nutrient absorption is temperature-sensitive [47]. Optimal enzyme activity for digestion is often temperature-specific, impacting the extent to which nutrients are absorbed during critical growth periods [47]. Early thermal experiences significantly shape the long-term health and nutritional status of aquatic organisms [48]. The effects of initial temperature exposure can have stable outcomes on tissue composition and overall health [48]. Overall, Koi fish were exposed to different temperatures after hatching (in the first phase, 60 dph) and showed different nutritional contents even after a period of constant temperature (in the second phase, 120 dph).

At the first phase (60 dph), the expression levels of five genes (HSP70, HSP90, BCL-2, SOD, and FASN) in Koi carp were significantly highest in T3 and T4, while MYOD genes showed significantly highest expression levels in T1. Fish possess the remarkable ability to adapt to varying temperatures through genetic modifications that enhance their thermal tolerance. Studies have revealed differences in gene expression related to respiratory processes among fish populations inhabiting distinct thermal environments, suggesting a genetic foundation for this adaptive capacity [49,50]. Our findings align with those of [51], who demonstrated that heat stress induced a tissue-specific and time-dependent increase in the expression of most Heat Shock Protein (HSP) genes in spotted seabass. Similarly, the filefish species, Thamnaconus septentrionalis, showed a significant increase in HSP70 and HSP90 gene expression under high and low salinity conditions and elevated temperatures [52]. In another study, bluegill sunfish exhibited significantly enhanced Superoxide Dismutase (SOD) activity when subjected to higher water temperatures [53]. Additionally, research on Mytilus edulis mussels revealed that exposure to elevated temperatures, in combination with metformin, can induce an increase in BCL-2 and FAS levels, leading to increased apoptosis (programmed cell death) in their gonads, highlighting a potential new environmental concern [54]. When fish are subjected to high temperatures, their cells become stressed, causing proteins to lose their normal shape and function [55]. To counteract this, a cellular defense mechanism called the “heat shock response” is triggered [56]. This response increases the production of special proteins known as Heat Shock Proteins, including HSP70 and HSP90 [57]. These proteins act like cellular repair crews, helping to refold damaged proteins and prevent them from clumping together, thus protecting the cell [57]. At the same time, another protein called BCL-2, which prevents cell death, is increased to help cells survive heat stress and maintain the health of tissues. Additionally, the body might boost the activity of FASN, a protein involved in making fats. This could help provide energy and repair cell membranes [58]. Because heat stress can also cause an imbalance known as oxidative stress (where harmful molecules called ROS are produced faster than the body can neutralize them), the body increases the production of antioxidant enzymes like SOD. This helps reduce oxidative damage and restore balance within the cells. Conversely, the activity of MYOD, a protein crucial for muscle growth, might be reduced. Elevated levels of inflammatory cytokines may disrupt signaling pathways essential for MYOD function [59]. This suggests that the fish’s body is prioritizing survival overgrowth, redirecting its resources to cope with the immediate threat of heat.

While all treatments applied constant elevated temperatures during the second phase (120 dph), significant increases in the expressions of HSP70, HSP90, BCL-2, MYOD, SOD, and FASN were observed in T1 and T2, respectively. In contrast, no significant differences in SOD and FASN levels were found between the treatment groups. Our findings are consistent with [51], who demonstrated that heat stress significantly upregulates several HSP genes in spotted seabass, such as HSP90aa1.1, HSP90aa1.2, HSP70.1, and HSP700.2. Similarly, Ref. [60] found that the anti-apoptotic gene BCL-2 is upregulated 24 h post-heat stress in corals, indicating a thermal stress response. These results support the crucial role of MYOD in the regulation of muscle protein synthesis and differentiation, as highlighted in [61]. Furthermore, Ref. [62] reported varied expression of MYOD1 paralogues in Atlantic salmon under different light conditions. The increase in MYOD1a expression alongside other muscle growth genes, contrasted with the decrease in MYOD1b and MYOD1c, implies that these paralogues have complex and possibly distinct functions in muscle development influenced by photoperiod. To cope with heat stress, organisms may undergo genetic changes that lead to physiological adaptations [63]. These adaptations can include modifications to metabolic rates, thermoregulation, or water retention [64]. Ultimately, these genetic changes improve an organism’s ability to survive and reproduce in elevated temperatures [64]. As an example, different Koi varieties exhibit different tolerances to heat, often correlated with their specific environments and ecological niches [65,66]. Moreover, Koi fish demonstrate metabolic plasticity, as highlighted by [27], meaning they can adjust their internal biochemical processes in response to temperature variations. This capacity enables them to maintain physiological balance under thermal stress, allowing crucial bodily functions to continue without excessive energy consumption. Over time, Koi fish have evolved the ability to acclimate to rising water temperatures.

There are strong positive correlations between HSP70, HSP90, BCL-2, FASN, and SOD gene expression levels at 60 dph. MYOD expression was negatively correlated with all genes. At 120 dph, HSP70, HSP90, and BCL-2 remained positively correlated. The observed resilience to temperature fluctuations in these fish may be due to genetic modifications. Specifically, alterations to genes encoding Heat Shock Proteins and other components of the stress response pathway could improve cellular stability during periods of high or low temperatures [67]. Furthermore, genetic engineering may promote faster growth rates in warmer waters, leading to quicker maturation [68]. This accelerated development is particularly valuable in aquaculture, where it can improve production efficiency. In a similar vein, the study reported in [32] highlighted the significant roles of myeloperoxidase, complement 3, and HSP70 in heat stress adaptation. Notably, HSP70 was identified as a sensitive biomarker for heat stress specifically in Koi carp. Emad’s study revealed differential responses to heat stress among three Koi strains. Ogon exhibited the strongest ability to withstand heat stress, Kohaku displayed moderate tolerance, and Showa Sanshoku was the most vulnerable. These results are consistent with practical observations in Koi farming. The study further posits that the low survival rate of Showa Sanshoku in culture may be attributed to persistent oxidative stress and immunosuppression experienced under stressful conditions [32]. The gene expression in the two phases changes to help fish adapt to temperature and perform all functions.

Our study found that, in the first phase (60 dph), the relationship between growth parameters and gene expression is complex, exhibiting both positive and negative associations. Notably, SGR, WG, GMW, and TGC demonstrate strong positive correlations with each other. Similarly, HSP70 and HSP90 exhibit a strong positive correlation. In contrast, SGR is moderately and negatively correlated with FASN, while displaying only a weak positive correlation with MYOD. In many studies, genes related to temperature stress, such as Heat Shock Proteins (HSPs), heat shock transcription factors (HSFs), and cytochrome P450 enzymes (CYPs), upregulate their expression in response to elevated temperatures in fish like common carp [69], rohu [70], rainbow trout [71], and zebrafish [72]. This upregulation often coincides with a depression in growth parameters, including Specific Growth Rate (SGR) and Weight Gain. When temperatures rise, cells produce Heat Shock Proteins (HSPs) for protection against damage [73]. However, while vital for survival during heat stress, increased HSP production can redirect cellular energy from growth to repair and maintenance [74]. This shift occurs because high temperatures disrupt normal metabolic functions, altering energy distribution [75]. Consequently, genes that promote stress responses are activated, while those responsible for growth and reproduction are suppressed. There are strong relationships between expressions of genes that are related to stress temperatures and the growth performance of Koi carp fish.

The second phase (120 dph) of the research highlighted the relationship between gene expression and growth in Koi carp under controlled temperature conditions. Growth indicators such as SGR, WG, and GMW were positively correlated with each other, and negatively correlated with HSP70 and HSP90. The study further examined the connection between gene expression and body composition in these fish. Their results indicated that crude protein levels were strongly and positively associated with MYOD, ash, and gross energy. On the other hand, significant negative correlations were found between crude protein and the expression of HSP70, HSP90, BCL2, and fat. Temperature fluctuations affect the proximate composition by altering nutrient uptake and metabolic processes [76]. For example, elevated temperatures might boost metabolism but can also degrade muscle tissue if energy demands are not met [77]; therefore, in our study, the gross energy increased in T3 and T4, where fish had already adapted to high temperatures from the first phase. Temperature stress also affects gene expression, which in turn impacts fish growth. Stressful temperatures can hinder growth as energy is redirected from growth to stress responses. Similarly, acclimatization to different temperatures significantly impacts both the growth rate and the expression of genes in the cold-water fish species Schizothorax prenanti [78]. Moreover, temperature plays a crucial role in fish physiology, drastically impacting their metabolism, feeding habits, digestive processes, and overall energy equilibrium. These effects vary among different fish species and are linked to their capacity to adjust to environmental temperature shifts via changes in their physical traits and bodily functions [76]. Investigating specific genes in Koi fish has yielded valuable insights into the link between gene expression and temperature adaptation. Analyzing how these genes respond to different temperatures allows us to better comprehend the adaptive processes that enable Koi populations to thrive in various environments.

In our study, at 120 Days Post-Hatching (120 dph), the amino acid profiles of Koi carp subjected to various treatments were analyzed. The analysis revealed significant variations in both essential and non-essential amino acid concentrations among the treatment groups. Treatment T4 exhibited the highest levels of total essential amino acids and total non-essential amino acids, whereas T1 showed the lowest total essential amino acid content. Furthermore, the impact of temperature on the fatty acid composition of Koi carp was investigated, demonstrating significant differences across treatments. Notably, Treatment T4 displayed the greatest concentrations of myristic, palmitic, and oleic acids, while Treatment T3 had the highest stearic acid levels. Our findings on fatty acid composition align with those reported by the authors of [79], who demonstrated that goldfish fed enriched artemia exhibited an improved fatty acid profile compared to a control group. A sudden rise in water temperature has varying impacts on amino acid metabolism in goldfish, specifically in the brain, liver, and muscle tissues [80]. Additionally, research indicates that fish exhibit improved dietary lipid efficiency and gut health when subjected to fluctuating temperatures. The optimal dietary lipid content for maximizing feed utilization and supporting intestinal health was determined to be 16% [81]. In response to gradual temperature fluctuations, fish undergo physiological acclimatization, including the modification of enzyme isoforms to optimize metabolic pathways for the given temperature range [82]. This ensures efficient nutrient utilization under varying thermal conditions. The active transport of amino acids across cell membranes is also temperature-dependent [83], with extreme temperatures potentially hindering transport efficiency unless acclimatization occurs, consequently impacting nutrient absorption and overall metabolic rate [6]. Moreover, temperature affects cell membrane composition; fish increase unsaturated fatty acid content in phospholipids at higher temperatures to maintain membrane fluidity and function and may increase saturated fatty acid content at lower temperatures to stabilize the membrane [84]. To ensure the continued success of Koi carp aquaculture, we must proactively address the challenges posed by climate change. Research efforts should specifically focus on how Koi carp adapt and grow under fluctuating temperature conditions, a key stressor associated with climate change. Understanding these adaptations is crucial for maintaining sustainable and resilient Koi carp aquaculture practices.

5. Conclusions

Climate change is a growing concern for aquaculture, particularly regarding its effect on temperature-sensitive species like Koi carp. Our research focused on the impact of fluctuating temperatures, a key climate change stressor, on Koi carp’s growth and adaptation. The results indicate that temperature significantly influences Koi carp growth rates, survival rates, and the activity of stress response and metabolic genes. Interestingly, early exposure to temperature variations may improve the ability of Koi carp to cope with future temperature stress. This research emphasizes the importance of understanding these temperature-related effects to inform future research and develop strategies to mitigate climate change impacts on Koi carp production in aquaculture.