1. Introduction
The Tibetan plateau (also known as the Qinghai-Tibet Plateau) is characterized by extreme environmental conditions, such as low oxygen concentration, high ultraviolet radiation intensity and daily temperature fluctuations. These conditions pose significant physiological challenges to animals [1,2]. Numerous genome-wide investigations on multiple species have uncovered multiple adaptive pathways that may be responsible for highland adaption in humans [3], snub-nosed monkeys [2], Tibetan mastiffs [4], fish [5] and so on. Nowadays, metabolite analysis is increasingly used to examine the ecological and evolutionary responses of plants to altitude gradients [6,7].
Metabolomics developed quickly in the twenty-first century due to the advancements in LC-MS and other technologies. Fiehn [8] established the link between metabolites and physiological function. By using high-throughput detection and data processing, metabolomics can identify the metabolites in biological samples under specific conditions or treatments. Analyzing all of the metabolites in the samples and then correlating their amounts to the features of the biological sample or treatment are the objectives of the analytical approach used in metabolomics studies [9]. In the fields of life sciences and environmental sciences, metabolomics is crucial for investigating biomarkers [10], disease diagnosis [11] and drug metabolism [12]. For instance, the metabolites changes under various feed or temperatures were used to examine the link between feed, breeding environment and metabolic function changes [13]. The measurement of farmed fish using metabolomics enables the evaluation of fish breeding, nutrition and metabolism, immunity and disease and quality control of aquatic products [14]. In addition, various biomarkers, such as polycyclic aromatic hydrocarbons (PAH) in bile, can assist in understanding the effects of environmental pollution on fish bodies and aquatic ecosystems as a biomonitoring model of water pollution [15,16]. In order to offer a fresh viewpoint on the physiological and biochemical studies of organisms in high-altitude environments, metabolomics was used in this research.
The fish family schizothoracinae has successfully adapted to the harsh environment of the Tibetan Plateau [17]. According to morphological characteristics, such as scales, pharyngeal teeth and barbels, the schizothoracine fishes can be divided into three grades: primitive, specialized and highly specialized grades [18]. Schizothorax nukiangensis, one of the primitive-grade schizothoracine fishes, is endemic to the Nujiang River. According to past research and the result of our scientific expedition, Schizothorax nukiangensis is widespread throughout the main stem of the Nujiang River, with an altitude range from 1000 to 3,800 m [19,20]. Due to its wide altitude range, S. nukiangensis is an ideal candidate for studying fish adaptation to high-altitude aquatic environments. In this work, the differential accumulation of metabolites in the liver of Schizothorax nukiangensis from different altitude habitats was studied. Based on different metabolite profiles and their enriched KEGG pathway analysis, we approach the changes in the metabolic function of fish in different altitude environments. Our research will provide both theoretical and practical insights into the high-altitude adaption of fish in the Qinghai-Tibet Plateau.
2. Materials and Methods
2.1. Fish Sampling
Fish were collected from 2 localities along the Nujiang River (Figure 1). Basu (BS) was chosen as the low-altitude site with coordinates of 30.09977° N and 97.23578° E and an elevation of 2100 m. Biru (BR) was chosen as the high-altitude site with coordinates of 31.4878° N and 93.77231° E with an elevation of 3890 m. Under the authorization of the fisheries departments of the Chinese government, we sampled S. nukiangensis in September and October 2020. We selected six S. nukiangensis with around 150 g weight and a healthy, symmetrical appearance at each site. After fish were anesthetized by 100 mg/L Tricaine methanesulfonate (MS-222, Sigma, St. Louis, MO, USA), the liver tissues were immediately placed into enzyme-free 1.5 mL EP tubes and subsequently frozen in liquid nitrogen. All tubes were kept at −80 °C until metabolite extraction.
During fish sampling, field observations provided data on water temperature, flow rate, altitude and dissolved oxygen levels. Water temperature, pH and dissolved oxygen were assessed using YSI with proper probes (YSI ProPlus, YSI Inc., Yellow Springs, OH, USA). Water flow velocity was calibrated three times using the LS300-A portable flow rate measuring instrument (Nanjing Zhuoma Electromechanical Co., Nanjing, China).
2.2. Metabolite Extraction and UPLC-MS Sample Preparation
The liver tissues were cut on dry ice (~10 mg) into an Eppendorf tube (2 mL). The tissue samples with 200 μL of dd-H2O and five ceramic beads were homogenized using the homogenizer. A total of 800 μL methanol/acetonitrile (1:1, v/v) was added to the homogenized solution for metabolite extraction. The mixture was centrifuged for 15 min (14,000× g, 4 °C). The supernatant was dried in a vacuum centrifuge. For LC-MS analysis, the samples were re-dissolved in 100 μL acetonitrile/water (1:1, v/v) solvent.
2.3. LC-MS/MS Analysis
Analyses were performed using a UHPLC (1290 Infinity LC, Agilent Technologies, Santa Clara, CA, USA) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600) in Shanghai Applied Protein Technology Co., Ltd. For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 µm column (Waters Corporation, Milford, MA, USA). In both ESI positive and negative polarity modes, the mobile phase contained A = 25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B = acetonitrile. The gradient was 85% B for 1 min and was linearly reduced to 65% in 11 min, and then was reduced to 40% in 0.1 min and kept for 4 min, and then increased to 85% in 0.1 min, with a 5 min re-equilibration period employed. For RPLC separation, a 2.1 × 100 mm ACQUIY UPLC HSS T3 1.8 µm column (waters, Ireland) was used. In ESI positive polarity mode, the mobile phase contained A = water with 0.1% formic acid and B = acetonitrile with 0.1% formic acid; and in ESI negative polarity mode, the mobile phase contained A = 0.5 mM ammonium fluoride in water and B = acetonitrile. The gradient was 1% B for 1.5 min and was linearly increased to 99% in 11.5 min and kept for 3.5 min. Then it was reduced to 1% in 0.1 min and a 3.4 min re-equilibration period was employed. The gradients were at a flow rate of 0.3 mL/min, and the column temperatures were kept constant at 25 °C. A 2 µL aliquot of each sample was injected.
The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature as 600 °C, IonSpray Voltage Floating (ISVF) ± 5500 V. In MS only acquisition, the instrument was set to acquire over the m/z range 60–1000 Da, and the accumulation time for the TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25–1000 Da, and the accumulation time for the product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information-dependent acquisition (IDA) with the high-sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 V ± 15 eV; declustering potential (DP), 60 V (+) and −60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10.
2.4. Data Processing and Statistical Analysis
The raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert before importing them into freely available XCMS software. For peak picking, the following parameters were used: centWave m/z = 25 ppm, peakwidth = c (10, 60) and prefilter = c (10, 100). For peak grouping, bw = 5, mzwid = 0.025 and minfrac = 0.5 were used. CAMERA (Collection of Algorithms of MEtabolite pRofile Annotation) was used for the annotation of isotopes and adducts. In the extracted ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept. The compound identification of metabolites was performed by comparing the accuracy m/z value (<25 ppm) and MS/MS spectra with an in-house database established with available authentic standards.
After being normalized to total peak intensity, the processed data were analyzed by R package (ropls), where it was subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The 7-fold cross-validation and response permutation testing were used to evaluate the robustness of the model. The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification. Metabolites with a VIP value > 1 were further applied to Student’s t-test at the univariate level to compare the significance of each metabolite; p-values less than 0.05 were considered statistically significant.
3. Results
3.1. Environment Indicators and Metabolomics
The dissolved oxygen, temperature, pH and flow velocity of water were detected during fish sampling three times. The results were 8.9 ± 0.2 mg/L in BS and 9 ± 0.3mg/L in BR for dissolved oxygen, 0.49 ± 0.06 m/s in BS and 0.3 ± 0.04 m/s in BR for flow velocity, 14.6 ± 0.2 °C in BS and 6.5 ± 0.1 °C in BR for water temperature and 6.85 ± 0.13 in BS and 7.56 ± 0.21 in BR for water pH based on our survey. After metabolomics analysis, a total of 366 metabolites were identified, including 243 metabolites in the positive polarity mode and 177 metabolites in the negative polarity mode.
3.2. PCA and OPLS-DA Analysis
According to the principal component analysis, the high-altitude and low-altitude groups were obviously distinct (Figure S1). OPLS-DA modeling could separate all samples into two distinct groups based on different altitudes as well (Figure S2). In S. nukiangensis liver, 25 metabolites showed lower relative abundance, and 8 metabolites showed higher relative abundance in BR compared with BS.
3.3. Different Metabolite Profiles
Based on the VIP in OPLS-DA model and p-value in Student’s t-test, metabolites with VIP > 1 and p < 0.05 were considered to be differentially accumulated metabolites (Table S1). The hierarchical clustering displayed that many metabolites were significantly different between high-altitude and low-altitude groups. For BR vs. BS, we observed 15 significantly different metabolites in the negative polarity mode (Figure 2) and 25 significantly different metabolites in the positive polarity mode (Figure 3). Two organic contaminants, nicotine and methoprene, were significantly different in the two groups. While methoprene was abundant in the high-altitude group, nicotine was substantial in the low-altitude group S. nukiangensis liver.
3.4. KEGG Pathway
The KEGG database (
4. Discussion
The Tibet section of the Nujiang River continuously drops down from the source, and the river is highly vulnerable to climate warming and environmental changes. Environmental indicator change will have a substantial impact on hydrological conditions, hydration properties and biological composition. In order to adapt to environmental changes at different altitudes, fish will alter their metabolic rate, which can be affected by temperature [21], water quality [22] and human activity [23]. Based on the result of environmental indicators, water temperature and water flow varied between the two sites. Environmental indicators such as ultraviolet were quite different with altitude change [2]. These environmental factors might impose evolutionary and adaptive pressure on schizothoracinae fish.
In high-altitude environments, the contents of polyunsaturated fatty acids, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in S. nukiangensis, were much lower. Fish synthesized very little EPA and DHA from dietary alpha-linolenic acid [24]. Most contents of fish EPA and DHA were mainly accumulated by diet. S. nukiangensis is among the omnivore and partial-carnivore fish, and chironomids are the major food for S. nukiangensis [25]. The biggest amount of variation in the chironomid assemblages was accounted for by air temperature among several other environmental factors [26]. Since the abundance of chironomids decreased as altitude increased due to the drop in temperature, a lack of food may be responsible for the low levels of EPA and DHA in a high-altitude habitat. Reduced levels of EPA and DHA in the liver may also be caused by higher rates of polyunsaturated n-3 fatty acid oxidation, enhanced mitochondrial and peroxisomal proliferation caused by 3-thia fatty acids [27]. EPA and DHA play important roles in disease prevention and catabolism of lipids [27,28,29]. Studies have confirmed that some polyunsaturated fatty acids could increase fish immunity [30]. A deficiency of DHA and EPA may have a negative effect on high-altitude S. nukiangensis.
Pentadecanoic acid (C15:0) is one of the significantly measurable odd-chain fatty acids that is also a saturated fatty acid. It can be utilized as a rough marker for dairy fat intake with regard to dietary analysis [31,32]. C15:0 has been shown to repair mitochondrial function, and its higher concentrations have also been linked to a lower risk of many diseases, such as adiposity and metabolic syndrome [33]. Meanwhile, it has been reported that C15:0 increases membrane fluidity to a similar extent as polyunsaturated fatty acids and is associated with a lower risk of developing multiple sclerosis. They proposed that the odd chain saturated fatty acids, including C15:0, were crucial for achieving a homeostatic range that satisfied the required conditions for membrane functionality [34]. According to our research, pentadecanoic acid levels in the liver of S. nukiangensis were significantly lower in high-altitude environments. This finding may be related to the lack of food and the subsequent need to sustain fish metabolism by altering fatty acid metabolism. Its decrease could suppress membrane fluidity and cannot maintain the necessary requirements of membrane functionality in fish liver.
However, the high-altitude group had higher levels of monounsaturated fatty acids, including oleic acid (C18:1) and myristoleic acid (C14:1). These outcomes differed from those of EPA, DHA and pentadecanoic acid. Some research revealed that toxicity and disease may affect organic oleic acid content [35,36]. Myristoleic acid is uncommon in nature, with limited research on its content in fish. However, it was confirmed to have antibiofilm and antimicrobial activities [37]. Numerous dietary and hormonal signals can affect stearoyl-CoA desaturase and then alter lipid metabolism [38]. Combining the results of high monounsaturated fatty acids, low pentadecanoic acid and low temperature in high altitude, we hypothesized that food and temperature may be the primary factors impacting their intake and metabolism in environments of varying altitudes.
Thioetheramide-PC serves as a competitive, reversible secretory phospholipase A2 inhibitor and is used to stabilize cells, provide energy and store substances [39]. Thioetheramide-PC enrichment may have an antiapoptotic effect on cells and antioxidant capacity, according to a recent study [40]. The rise in apoptotic and oxidative stress could be the cause of its low concentrations in high-altitude fish.
Significantly lower glycerophospholipid metabolites, including 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, 1-Stearoyl-sn-glycerol-3-phosphocholine, 1-Myristoyl-sn-glycero-3-phosphocholine and 1-Palmitoyl-sn-glycero-3-phospho, were observed in high-altitude S. nukiangensis. Similar results were found in lenok after suffering acute and lethal heat stress, which also was accompanied by many amino acids being significantly decreased [41]. The primary lipids in cell membranes are glycerophospholipids, which are crucial for controlling the potential and curvature of the membrane as well as ion transport [42,43]. Changes in the structure and function of cellular membranes in fish species may be indicated by the downregulation of glycerophospholipids in association with oxidative stress [44]. Oxidative stress is induced by reactive oxygen species (ROS), which increases when environmental pressure rises and causes cellular component damage [45]. Oxidative stress in fish can be directly affected by a multitude of stressors, such as temperature fluctuations, UV exposure and pollution [46,47]. We concluded, based on the significantly lower glycerophospholipid metabolites levels and the unpublished transcriptomic analysis result, that S. nukiangensis experienced more severe oxidative stress in BR as a result of high UV exposure and low temperature in a high-altitude environment.
A signaling cascade was triggered by the phosphorylation of eIF2 and shields cells from the metabolic effects of ER oxidation to oxidative stress resistance by amino acid metabolism [48]. There was a correlation between oxidative stress and amino acid metabolism in patients with depression [49]. Amino acids and amino acid derivatives, such as L-Arginine, L-Pipecolic acid, D-aspartic acid, L-Methionine, 2-Oxoadipic acid and dipeptides, such as Pro-Ala, Pro-Val, Gly-Arg, Lys-Pro, Pro-Ser and Leu-Ala, were all significantly lower in high-altitude fish. All these findings implied a reduction in protein content. This result supports the occurrence of low protein levels in organisms in high-altitude environments [50]. S-Methyl-5’-thioadenosine, the byproduct of methionine metabolism, was located at the crossroads of cellular metabolism. It can regulate various pathways, including the inhibition of cell proliferation, lymphocyte activation, tumor development and apoptosis [51,52]. Creatine is a product of arginine and proline metabolism. Creatine can be converted into phosphorylcreatine by creatine kinase. After the degradation of ATP, the phosphate group of phosphorylcreatine can be transferred to ADP, turning it back into ATP to provide energy to the cell [53]. As oxidative substrates in numerous tissues, amino acids play significant roles in energy metabolism [54]. We concluded that the elevated levels of S-Methyl-5’-thioadenosine and creatine in the BR group indicate the improvement in amion acid metabolism and energy supply in the liver of fish inhabiting high-altitude environments. In addition, creatine was found to have anticancer, antiviral and anti-diabetic characteristics, as well as the potential to protect tissues from hypoxia, ischemia, neurodegeneration or muscle injury [55]. Consequently, despite the source of energy, S. nukiangensis has a high creatine concentration that may be beneficial for fish inhabiting large aquatic environments.
Apart from the metabolism of amino acids, the two groups differed in their utilization of D-mannose-6-phosphate and D-mannose-1-phosphate in fructose and mannose metabolism. With an increase in altitude, they were all significantly elevated in the liver of fish. D-mannose-6-phosphate is formed by the hexokinase-derived phosphorylation of mannose [56]. Phosphomannomutases can convert mannose-6-phosphate to mannose-1-phosphate, which is the immediate precursor for GDP-mannose and the activated donor for mannose addition [57]. They both engage in energy metabolic processes, such as gluconeogenesis, glycolysis and TCA [58]. A similar result was indicated in tilapia fed a different diet [59]. The biosynthesis of polysaccharides was reduced in a high-fat diet group, which may affect energy metabolism and the formation of various structural substances in fish. Our results indicated that carbohydrate and energy metabolism and glycolysis pathways are related to fish energy supply and structural substance formation in different environments.
The most prevalent alkaloid in tobacco is nicotine, which is also found in the Solanaceae family of edible plants, such as potatoes, tomatoes and eggplants [60,61]. Meanwhile, nicotine is the most abundant and accounts for widespread human use of tobacco products throughout the world. With roughly 1.3 billion users globally, nicotine is one of the most often used legal medications because of its pandemic, addictive and non-pharmacological qualities [62]. In agriculture, the development of insecticides using nicotine as a raw ingredient and the extensive use of nicotine in agriculture led to a rise in nicotine levels in surface water [63]. According to our findings, the difference in nicotine content between the livers of the two groups is enormous. Significantly more nicotine was in the BS group than in the BR group. Due to the rising production and consumption of nicotine, limited removal from wastewater treatment, and potential environmental toxicity, nicotine and its metabolites can be categorized as emerging contaminants [64]. Through a variety of mechanisms, these chemical substances and their active metabolites may be continuously delivered into the aquatic environment [65]. The source of nicotine was unknown in this research. Based on the result, we proposed that the source is the excretion, manufacture and direct disposal of humans who smoke, then nicotine was soluble in the wastewater and flowed into the Nujiang River.
Methoprene is a pesticide that can be directly applied to aquatic systems in order to limit the number of mosquito larvae and, consequently, the transmission of pathogens that mosquitoes vector to humans and other animals [66]. It is also widely utilized in the production of foods, such as beef, milk, mushrooms, peanuts, rice and cereals [67]. Methoprene does not bioaccumulate in fish and does not persist in the environment, for it undergoes rapid degradation and metabolism in plants, animals and aquatic microorganisms [68,69]. However, methoprene is quite toxic to aquatic life [70]. Additionally, as glaciers form and melt, organic pollutants travel and accumulate on the Qinghai-Tibet Plateau and are then released into water bodies [71,72]. Furthermore, high-trophic organisms are enriched with organic contaminants, particularly in the North and South Poles and the Qinghai-Tibet Plateau. Due to the lack of food, fish have a higher concentration of organic pollutants in these areas [73]. Temperature increases can promote biological activity and accelerate the decomposition of organic pollutants, thus reducing the accumulation of organic pollutants [74]. Methoprene concentration rose in BR, which is located upstream of the Nujiang River, according to our findings. There were two probable explanations for the event. The increased content of methoprene in upstream water may be attributable to glaciers forming and melting. Methoprene rapidly degraded due to the rise in temperature downstream, which led to a low concentration in fish. The other explanation might be the food intake of S. nukiangensis due to the comparative lack of food in BR for the low temperature.
The mechanistic target of rapamycin (mTOR) signaling system detects and integrates several environmental inputs to control organismal development and homeostasis. Numerous important cellular activities are regulated by this route, which is also implicated in an increasing number of diseases [75]. Two mTOR complexes, mTORC1 and mTORC2, positively regulate cellular metabolism and ATP production [76]. They also increase hepatic lipogenesis and protein synthesis, and inhibit lipolysis and autophagy in tissue. Based on the low concentration of amino acids in BR, the shortage of amino acids deactivates mTORC1 and limits protein synthesis in high-altitude fish, which is followed by an increase in autophagy and apoptosis. Apoptosis and autophagy can protect and recycle cellular resources, limit tissue damage, restore tissue integrity, terminate inflammatory reactions and increase adaptability [77]. Autophagy plays an important role in cell metabolic stress, cell differentiation, growth and development and prevention of DNA damage processes [78]. Autophagy activation can minimize cell damage induced by external stress [79]. Apoptosis is a component of the quality control and repair system in living organisms, and it also regulates developmental defects and contributes to the plasticity of the organism during development [80]. Apoptosis occurs in fish in response to environmental stress [81]. There are numerous causes for the activation of apoptotic pathways, such as DNA damage or uncontrolled cell proliferation [82]. Lysosome is also pivotally involved in this process of apoptosis, and the existence of a ‘lysosomal pathway of apoptosis’ is now generally accepted [83]. Therefore, apoptosis, lysosome and autophagy were beneficial to S. nukiangensis in adaptation to the intense UV and cold temperatures at high altitudes for their rules as quality control and repair systems in fish.
As discussed, S. nukiangensis, in high-altitude environments, showed low concentrations in amino acids, polyunsaturated fatty acids, saturated fatty acid and glycerophospholipid metabolites, high concentrations in S-Methyl-5’-thioadenosine, creatine, D-mannose-6-phosphate, D-mannose-1-phosphate and monounsaturated fatty acids. These differential metabolites were involved in oxidative stress, energy metabolism, carbohydrate metabolism and lipid metabolism. mTOR signaling pathway, apoptosis and lysosome were the KEGG pathways that were enriched between different altitude groups to ensure energy supply and reduce cell damage in high-altitude fish. All these results contributed to the understanding of the high-altitude adaptation of S. nukiangensis in the Nujiang River. Nicotine and methoprene, two organic pollutants, performed differently in fish at high and low altitudes. Further research is necessary to assess the state of organic contamination in the Nujiang River water and its effects on fish and the ecosystem.
W.X.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing—original draft, Writing—review and editing. F.Z.: Investigation, Validation, Methodology. D.W.: Conceptualization, Validation, Methodology, Writing—original draft. D.C.: Funding acquisition, Project administration, Supervision, Validation, Writing—original draft. X.D.: Conceptualization, Validation, Methodology, Writing—original draft. M.L.: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing—original draft, Writing—review and editing. D.L.: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing—original draft, Writing—review and editing. All authors have read and agreed to the published version of the manuscript.
The animal study protocol was approved by the Ethics Committee of Huazhong Agricultural University (protocol code is HZAUFI-2019-034 and the date is 25 February 2019). All efforts were made to minimize the suffering of sampled fish.
Not applicable.
We thank Shaoping Liu, Huiwu Tian, Lei Gao and Ke Liu for suggestions to improve the paper. We thank the following for assistance in the field: Jie Zhang, Yizhu Chen, Junying Zhang and Rongjun Ma.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. Differentially accumulated metabolites in the liver between two groups in the negative polarity mode (BR vs. BS).
Enlarge this image.Figure 3. Differentially accumulated metabolites in the liver between two groups in the positive polarity mode (BR vs. BS).
Enlarge this image.Figure 4. KEGG pathway enrichment analysis of the differentially accumulated metabolites in BR compared with BS.
Supplementary Materials
The following are available online at
References
1. Cheviron, Z.A.; Brumfield, R.T. Genomic insights into adaptation to high-altitude environments. Heredity; 2012; 108, pp. 354-361. [DOI: https://dx.doi.org/10.1038/hdy.2011.85] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21934702]
2. Zhou, X.; Meng, X.; Liu, Z.; Chang, J.; Wang, B.; Li, M.; Wengel, P.O.; Tian, S.; Wen, C.; Wang, Z. et al. Population genomics reveals low genetic diversity and adaptation to hypoxia in snub-nosed monkeys. Mol. Biol. Evol.; 2016; 33, pp. 2670-2681. [DOI: https://dx.doi.org/10.1093/molbev/msw150] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27555581]
3. Wang, G.D.; Fan, R.X.; Zhai, W.; Liu, F.; Wang, L.; Zhong, L.; Wu, H.; Yang, H.C.; Wu, S.F.; Zhu, C.L. et al. Genetic convergence in the adaptation of dogs and humans to the high-altitude environment of the Tibetan Plateau. Genome Biol. Evol.; 2014; 6, pp. 2122-2128. [DOI: https://dx.doi.org/10.1093/gbe/evu162] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25091388]
4. Miao, B.P.; Wang, Z.; Li, Y.X. Genomic analysis reveals hypoxia adaptation in the Tibetan Mastiff by introgression of the gray wolf from the Tibetan Plateau. Mol. Phylogenet. Evol.; 2017; 34, pp. 734-743.
5. Yang, L.; Wang, Y.; Zhang, Z.; He, S. Comprehensive transcriptome analysis reveals accelerated genic evolution in a Tibet fish, Gymnodiptychus pachycheilus. Genome Biol. Evol.; 2015; 7, pp. 251-261. [DOI: https://dx.doi.org/10.1093/gbe/evu279]
6. Yang, C.; Yang, H.; Xu, Q.; Wang, Y.; Sang, Z.; Yuan, H. Comparative metabolomics analysis of the response to cold stress of resistant and susceptible Tibetan hulless barley (Hordeum distichon). Phytochemistry; 2020; 174, 112346. [DOI: https://dx.doi.org/10.1016/j.phytochem.2020.112346]
7. Liu, F.; Liu, P.; Zhang, Y.; Sun, L.; Zhang, P.; Cao, M.; Zhou, H.; Wang, W.; Xu, J. Comparative metabolomics reveals that Agaricus bisporus fairy ring modulates the growth of alpine meadow plant on the Qinghai-Tibet Plateau. Ecol. Indic.; 2021; 129, 107865. [DOI: https://dx.doi.org/10.1016/j.ecolind.2021.107865]
8. Fiehn, O. Metabolomics—The link between genotypes and phenotypes. Functional Genomics; Springer: Berlin/Heidelberg, Germany, 2002; pp. 155-171.
9. Kell, D.B. Metabolomics and systems biology: Making sense of the soup. Curr. Opin. Microbiol.; 2004; 7, pp. 296-307. [DOI: https://dx.doi.org/10.1016/j.mib.2004.04.012]
10. Griffin, J.L.; Rosiers, C.D. Applications of metabolomics and proteomics to the mdx mouse model of Duchenne muscular dystrophy: Lessons from downstream of the transcriptome. Genome Med.; 2009; 1, 32. [DOI: https://dx.doi.org/10.1186/gm32]
11. Holmes, E.; Wilson, I.D.; Nicholson, J.K. Metabolic phenotyping in health and disease. Cell; 2008; 134, pp. 714-717. [DOI: https://dx.doi.org/10.1016/j.cell.2008.08.026]
12. Wilson, I.D. Drugs, bugs, and personalized medicine: Pharmacometabonomics enters the ring. Proc. Natl. Acad. Sci. USA; 2009; 106, pp. 14187-14188. [DOI: https://dx.doi.org/10.1073/pnas.0907721106] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19706501]
13. Silva, T.S.; da Costa, A.M.R.; Conceicao, L.E.C.; Dias, J.P.; Rodrigues, P.M.L.; Richard, N. Metabolic fingerprinting of gilthead seabream (Sparus aurata) liver to track interactions between dietary factors and seasonal temperature variations. PeerJ; 2014; 2, e527. [DOI: https://dx.doi.org/10.7717/peerj.527]
14. Alfaro, A.C.; Young, T. Showcasing metabolomic applications in aquaculture: A review. Rev. Aquacult.; 2016; 10, pp. 135-152. [DOI: https://dx.doi.org/10.1111/raq.12152]
15. Van der Oost, R.; Beyer, J.; Vermeulen, N.P.E. Fish bioaccumulation and biomarkers in environmental risk assessment: A review. Environ. Toxicol. Phar.; 2003; 13, pp. 57-149. [DOI: https://dx.doi.org/10.1016/S1382-6689(02)00126-6]
16. Zhou, Q.; Zhang, J.; Fu, J.; Shi, J.; Jiang, G. Biomonitoring: An appealing tool for assessment of metal pollution in the aquatic ecosystem. Anal. Chim. Acta; 2008; 606, pp. 135-150. [DOI: https://dx.doi.org/10.1016/j.aca.2007.11.018]
17. He, D.K.; Chen, Y.F.; Chen, Y.Y.; Chen, Z.M. Molecular phylogeny of the specialized schizothoracine fishes (Teleostei: Cyprinidae), with their implications for the uplift of the Qinghai-Tibetan Plateau. Chin. Sci. Bull.; 2004; 49, pp. 39-48. [DOI: https://dx.doi.org/10.1007/BF02901741]
18. He, D.K.; Chen, Y.F. Biogeography and molecular phylogeny of the genus Schizothorax (Teleostei: Cyprinidae) in China inferred from cytochrome b sequences. J. Biogeogr.; 2006; 33, pp. 1448-1460. [DOI: https://dx.doi.org/10.1111/j.1365-2699.2006.01510.x]
19. Chen, W.; Du, K.; He, S. Genetic structure and historical demography of Schizothorax nukiangensis (Cyprinidae) in continuous habitat. Ecol. Evol.; 2015; 5, pp. 984-995. [DOI: https://dx.doi.org/10.1002/ece3.1413]
20. Chen, W.; Yue, X.; He, S. Genetic differentiation of the Schizothorax species complex (Cyprinidae) in the Nujiang River (upper Salween). Sci. Rep.; 2017; 7, 5944. [DOI: https://dx.doi.org/10.1038/s41598-017-06172-5]
21. Claireaux, G.; Lagardère, J.P. Influence of temperature, oxygen and salinity on the metabolism of the European sea bass. J. Sea Res.; 1999; 42, pp. 157-168. [DOI: https://dx.doi.org/10.1016/S1385-1101(99)00019-2]
22. Meador, J.P.; Bettcher, L.F.; Ellenberger, M.C.; Senn, T.D. Metabolomic profiling for juvenile Chinook salmon exposed to contaminants of emerging concern. Sci. Total Environ.; 2020; 747, 141097. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.141097] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32781313]
23. Izral, N.M.; Brua, R.B.; Culp, J.M.; Yates, A.G. Crayfish tissue metabolomes effectively distinguish impacts of wastewater and agriculture in aquatic ecosystems. Sci. Total Environ.; 2021; 760, 143322. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.143322] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33218799]
24. National Research Council. Nutrient Requirements of Fish and Shrimp; National Academies Press: Washington, DC, USA, 2011.
25. Wang, Q.; Liu, M.D.; Zhu, F.Y.; Li, Z.; Liu, S.P.; Duan, X.B.; Chen, D.Q.; Yang, R.B. Comparative study of three species of Schizothoracine on feeding and digestive organs in upper Nujiang River. Chinese J. Zool.; 2019; 54, pp. 207-221. (In Chinese with English Abstract)
26. Fortin, M.C.; Medeiros, A.S.; Gajewski, K.; Barley, E.M.; Larocque-Tobler, I.; Porinchu, D.F.; Wilson, S.E. Chironomid-environment relations in northern north America. J. Paleolimnol.; 2015; 54, pp. 223-237. [DOI: https://dx.doi.org/10.1007/s10933-015-9848-0]
27. Kurihara, T.; Adachi, Y.; Yamagata, M.; Abe, K.; Akimoto, M.; Hashimoto, H.; Ishiguro, H.; Niimi, A.; Maeda, A.; Shigemoto, M. Role of eicosapentaenoic acid in lipid metabolism in the liver, with special reference to experimental fatty liver. Clin. Ther.; 1994; 16, pp. 830-837.
28. Flachs, P.; Rossmeisl, M.; Kopecky, J. The effect of n-3 fatty acids on glucose homeostasis and insulin sensitivity. Physiol. Res.; 2014; 63, pp. S93-S118. [DOI: https://dx.doi.org/10.33549/physiolres.932715]
29. Masoodi, M.; Kuda, O.; Rossmeisl, M.; Flachs, P.; Kopecky, J. Lipid signaling in adipose tissue: Connecting inflammation & metabolism. BBA-Mol. Cell Biol. Lipids; 2015; 1851, pp. 503-518.
30. Kong, L.; Cheng, S.; Xiang, X.; Liu, W.; Yu, D.; Yang, Y.; Zhou, J.; Huang, F.; Dong, G. Dietary conjugated linoleic acid modulates morphology, selective immune parameters, and gene expressions in the intestine of grass carp. Fish Shellfish Immun.; 2019; 86, pp. 536-548. [DOI: https://dx.doi.org/10.1016/j.fsi.2018.11.071]
31. Hughes, R. Definitions for public health nutrition: A developing consensus. Public Health Nutr.; 2003; 6, pp. 615-620. [DOI: https://dx.doi.org/10.1079/PHN2003487]
32. Brevik, A.; Veierød, M.B.; Drevon, C.A.; Andersen, L.F. Evaluation of the odd fatty acids 15:0 and 17:0 in serum and adipose tissue as markers of intake of milk and dairy fat. Eur. J. Clin. Nutr.; 2005; 59, pp. 1417-1422. [DOI: https://dx.doi.org/10.1038/sj.ejcn.1602256]
33. Dąbrowski, G.; Konopka, I. Update on food sources and biological activity of odd-chain, branched and cyclic fatty acids—A review. Trends Food Sci. Technol.; 2021; 119, pp. 514-529. [DOI: https://dx.doi.org/10.1016/j.tifs.2021.12.019]
34. Holman, R.T.; Adams, C.E.; Nelson, R.A.; Grater, S.J.; Jaskiewicz, J.A.; Johnson, S.B.; Erdman, J.W., Jr. Patients with anorexia nervosa demonstrate deficiencies of selected essential fatty acids, compensatory changes in nonessential fatty acids and decreased fluidity of plasma lipids. J. Nutr.; 1995; 125, pp. 901-907. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/7722693]
35. Abdullah, M.; Kornegay, J.N.; Honcoop, A.; Parry, T.L.; Balog-Alvarez, C.J.; O’Neal, S.K.; Bain, J.R.; Muehlbauer, M.J.; Newgard, C.B.; Patterson, C. et al. Non-targeted metabolomics analysis of golden retriever muscular dystrophy-affected muscles reveals alterations in arginine and proline metabolism, and elevations in glutamic and oleic acid in vivo. Metabolites; 2017; 7, 38. [DOI: https://dx.doi.org/10.3390/metabo7030038] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28758940]
36. Lu, Y.; Wang, G.; Hao, H.; Huang, Q.; Yan, B.; Zha, W.; Gu, S.; Ren, H.; Zhang, Y.; Fan, X. et al. Gas chromatography/time-of-flight mass spectrometry based metabonomic approach to differentiating hypertension- and age- related metabolic variation in spontaneously hypertensive rats. Rapid Commun. Mass Spectrom.; 2008; 22, pp. 2882-2888. [DOI: https://dx.doi.org/10.1002/rcm.3670] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18720470]
37. Kim, Y.G.; Lee, J.H.; Lee, J. Antibiofilm activities of fatty acids including myristoleic acid against Cutibacterium acnes via reduced cell hydrophobicity. Phytomedicine; 2021; 91, 153710. [DOI: https://dx.doi.org/10.1016/j.phymed.2021.153710] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34461422]
38. Piccinin, E.; Cariello, M.; De Santis, S.; Ducheix, S.; Sabbà, C.; Ntambi, J.M.; Moschetta, A. Role of oleic acid in the gut-liver axis: From diet to the regulation of its synthesis via stearoyl-CoA desaturase 1 (SCD1). Nutrients; 2019; 11, 2283. [DOI: https://dx.doi.org/10.3390/nu11102283]
39. Sokolowska, M.; Stefanska, J. Cytosolic phospholipase A2 group IVA influence on GM-CSF expression in human lung cells: A pilot study. Med. Sci. Monitor; 2010; 16, pp. BR300-BR306.
40. Gao, E.M.; Turathum, B.; Wang, L.; Zhang, D.; Liu, Y.B.; Tang, R.X.; Chian, R.C. The differential metabolomes in cumulus and mural granulosa cells from Human preovulatory follicles. Reprod. Sci.; 2022; 29, pp. 1343-1356. [DOI: https://dx.doi.org/10.1007/s43032-021-00691-3]
41. Chen, Y.; Liu, Y.; Bai, Y.; Xu, S.; Yang, X.; Cheng, B. Intestinal metabolomics of juvenile lenok (Brachymystax lenok) in response to heat stress. Fish Physiol. Biochem.; 2022; 48, pp. 1389-1400. [DOI: https://dx.doi.org/10.1007/s10695-022-01128-7]
42. Shimura, M.; Shindou, H.; Szyrwiel, L.; Tokuoka, S.M.; Hamano, F.; Matsuyama, S.; Okamoto, M.; Matsunaga, A.; Kita, Y.; Ishizaka, Y. et al. Imaging of intracellular fatty acids by scanning X-ray fluorescence microscopy. FASEB J.; 2016; 30, pp. 4149-4158. [DOI: https://dx.doi.org/10.1096/fj.201600569R]
43. Zhang, W.; Song, Y.; Chai, T.T.; Liao, G.Q.; Zhang, L.; Jia, Q.; Qian, Y.Z.; Qiu, J. Lipidomics perturbations in the brain of adult zebrafish (Danio rerio) after exposure to chiral ibuprofen. Sci. Total Environ.; 2020; 713, 136565. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.136565]
44. Melvin, S.D.; Lanctot, C.M.; Doriean, N.J.C.; Bennett, W.; Carroll, A.R. NMR-based lipidomics of fish from a metal (loid) contaminated wetland show differences consistent with effects on cellular membranes and energy storage. Sci. Total Environ.; 2019; 654, pp. 284-291. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2018.11.113] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30445328]
45. Lushchak, V.I. Environmentally induced oxidative stress in aquatic animals. Aquat. Toxicol.; 2011; 101, pp. 13-30. [DOI: https://dx.doi.org/10.1016/j.aquatox.2010.10.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21074869]
46. Birnie-Gauvin, K.; Costantini, D.; Cooke, S.J.; Willmore, W.G. A comparative and evolutionary approach to oxidative stress in fish: A review. Fish Fish.; 2017; 18, pp. 928-942. [DOI: https://dx.doi.org/10.1111/faf.12215]
47. Hurem, S.; Fraser, T.W.K.; Gomes, T.; Mayer, I.; Christensen, T. Sub-lethal UV radiation during early life stages alters the behaviour, heart rate and oxidative stress parameters in zebrafish (Danio rerio). Ecotoxicol. Environ. Safe.; 2018; 166, pp. 359-365. [DOI: https://dx.doi.org/10.1016/j.ecoenv.2018.09.082] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30278398]
48. Harding, H.P.; Zhang, Y.; Zeng, H.; Novoa, I.; Lu, P.D.; Calfon, M.; Sadri, N.; Yun, C.; Popko, B.; Paules, R. et al. An integrated stress response regulates amino acid metabolism and resistance to oxidative stress. Mol. Cell; 2003; 11, pp. 619-633. [DOI: https://dx.doi.org/10.1016/S1097-2765(03)00105-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12667446]
49. Wang, M.; Gui, X.; Wu, L.; Tian, S.; Wang, H.; Xie, L.; Wu, W. Amino acid metabolism, lipid metabolism, and oxidative stress are associated with post-stroke depression: A metabonomics study. BMC Neurol.; 2020; 20, 250. [DOI: https://dx.doi.org/10.1186/s12883-020-01780-7]
50. Gu, Z.; Li, L.; Tang, S.; Liu, C.; Fu, X.; Shi, Z.; Mao, H. Metabolomics reveals that crossbred dairy buffaloes are more thermotolerant than Holstein cows under chronic heat stress. J. Agr. Food Chem.; 2018; 66, pp. 12889-12897. [DOI: https://dx.doi.org/10.1021/acs.jafc.8b02862]
51. Williams-Ashman, H.G.; Seidenfeld, J.; Galletti, P. Trends in the biochemical pharmacology of 5’-deoxy-5’-methylthioadenosine. Biochem. Pharmacol.; 1982; 31, pp. 277-288. [DOI: https://dx.doi.org/10.1016/0006-2952(82)90171-X]
52. Mato, J.M.; Corrales, F.J.; Lu, S.C.; Avila, M.A. S-Adenosylmethionine: A control switch that regulates liver function. Faseb J.; 2002; 16, pp. 15-26. [DOI: https://dx.doi.org/10.1096/fj.01-0401rev]
53. Sahlin, K.; Harris, R.C. The creatine kinase reaction: A simple reaction with functional complexity. Amino Acids; 2011; 40, pp. 1363-1367. [DOI: https://dx.doi.org/10.1007/s00726-011-0856-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21394603]
54. Ballantyne, J.S. Amino acid metabolism. Fish Physiol.; 2001; 20, pp. 77-107.
55. Wyss, M.; Kaddurah-Daouk, R. Creatine and creatinine metabolism. Physiol. Rev.; 2000; 80, pp. 1107-1213. [DOI: https://dx.doi.org/10.1152/physrev.2000.80.3.1107]
56. Hu, X.; Shi, Y.; Zhang, P.; Miao, M.; Zhang, T.; Jiang, B. D-Mannose: Properties, production, and applications: An overview. Comprehen. Rev. Food Sci. Food Saf.; 2016; 15, pp. 773-785. [DOI: https://dx.doi.org/10.1111/1541-4337.12211]
57. Freeze, H.H. Genetic defects in the human glycome. Nat. Rev. Genet.; 2006; 7, pp. 537-551. [DOI: https://dx.doi.org/10.1038/nrg1894] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16755287]
58. McElroy, G.S.; Reczek, C.R.; Reyfman, P.A.; Mithal, D.S.; Horbinski, C.M.; Chandel, N.S. NAD+ regeneration rescues lifespan, but not ataxia, in a mouse model of brain mitochondrial complex I dysfunction. Cell Metab.; 2020; 32, pp. 301-308. [DOI: https://dx.doi.org/10.1016/j.cmet.2020.06.003] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32574562]
59. Tao, Y.F.; Qiang, J.; He, J.; Zhu, H.J.; Bao, J.W.; Xu, P. Untargeted LC-MS metabolomics approach reveals metabolic changes in genetically improved farmed tilapia (Oreochromis niloticus) with fatty liver induced by a high-fat diet. Aquac. Res.; 2021; 52, pp. 724-735. [DOI: https://dx.doi.org/10.1111/are.14929]
60. Wu, W.; Ashley, D.L.; Watson, C.H. Determination of nicotine and other minor alkaloids in international cigarettes by solid-phase microextraction and gas chromatography/mass spectrometry. Anal. Chem.; 2002; 74, pp. 4878-4884. [DOI: https://dx.doi.org/10.1021/ac020291p]
61. Siegmund, B.; Leitner, E.; Pfannhauser, W. Determination of the nicotine content of various edible nightshades (Solanaceae) and their products and estimation of the associated dietary nicotine intake. J. Agr. Food Chem.; 1999; 47, pp. 3113-3120. [DOI: https://dx.doi.org/10.1021/jf990089w]
62. Berlin, I.; Singleton, E.G.; Pedarriosse, A.M.; Lancrenon, S.; Rames, A.; Aubin, H.; Niaura, R. The modified reasons for smoking scale: Factorial structure, gender effects and relationship with nicotine dependence and smoking cessation in French smokers. Addiction; 2003; 98, pp. 1575-1583. [DOI: https://dx.doi.org/10.1046/j.1360-0443.2003.00523.x]
63. Ebele, A.J.; Oluseyi, T.; Drage, D.S.; Harrad, S.; Abdallah, M.A. Occurrence, seasonal variation and human exposure to pharmaceuticals and personal care products in surface water, groundwater and drinking water in Lagos State, Nigeria. Emerg. Contam.; 2020; 6, pp. 124-132. [DOI: https://dx.doi.org/10.1016/j.emcon.2020.02.004]
64. Pal, R.; Megharaj, M.; Kirkbride, K.P.; Naidu, R. Illicit drugs and the environment-a review. Sci. Total Environ.; 2013; 463, pp. 1079-1092. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2012.05.086] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22726813]
65. Verovšek, T.; Heath, D.; Heath, E. Occurrence, fate and determination of tobacco (nicotine) and alcohol (ethanol) residues in waste-and environmental waters. Trends Environ. Anal.; 2022; 34, e00164. [DOI: https://dx.doi.org/10.1016/j.teac.2022.e00164]
66. Lawler, S.P. Environmental safety review of methoprene and bacterially-derived pesticides commonly used for sustained mosquito control. Ecotoxicol. Environ. Safe.; 2017; 139, pp. 335-343. [DOI: https://dx.doi.org/10.1016/j.ecoenv.2016.12.038]
67. Henrick, C.A. Methoprene. J. Am. Mosq. Control Assoc.; 2007; 23, pp. 225-239. [DOI: https://dx.doi.org/10.2987/8756-971X(2007)23[225:M]2.0.CO;2]
68. Ross, D.H.; Judy, D.; Jacobson, B.; Howell, R. Methoprene concentrations in freshwater microcosms treated with sustained-release Altosid (r) formulations. J. Am. Mosq. Control Assoc.; 1994; 10, pp. 202-210.
69. Henrick, C.A.; Ko, J.; Nguyen, J.; Burleson, J.; Lindahl, G.; Van Gundy, D.; Edge, J.M. Investigation of the relationship between s-methoprene and deformities in anurans. J. Am. Mosq. Control Assoc.; 2002; 18, pp. 214-221.
70. Hurst, T.P.; Kay, B.H.; Ryan, P.A.; Brown, M.D. Sublethal effects of mosquito larvicides on swimming performance of larvivorous fish Melanotaenia duboulayi (Atheriniformes: Melanotaeniidae). J. Econ. Entomol.; 2007; 100, pp. 61-65. [DOI: https://dx.doi.org/10.1093/jee/100.1.61]
71. Ren, J.; Wang, X.; Wang, C.; Gong, P.; Yao, T. Atmospheric processes of organic pollutants over a remote lake on the central Tibetan Plateau: Implications for regional cycling. Atmos. Chem. Phys.; 2017; 17, pp. 1401-1415. [DOI: https://dx.doi.org/10.5194/acp-17-1401-2017]
72. Wang, X.; Wang, C.; Zhu, T.; Gong, P.; Fu, J.; Cong, Z. Persistent organic pollutants in the polar regions and the Tibetan Plateau: A review of current knowledge and future prospects. Environ. Pollut.; 2019; 248, pp. 191-208. [DOI: https://dx.doi.org/10.1016/j.envpol.2019.01.093]
73. Villa, S.; Migliorati, S.; Monti, G.S.; Holoubek, I.; Vighi, M. Risk of POP mixtures on the Arctic food chain. Environ. Toxicol. Chem.; 2017; 36, pp. 1181-1192. [DOI: https://dx.doi.org/10.1002/etc.3671] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28054401]
74. Spongberg, A.L.; Witter, J.D. Pharmaceutical compounds in the wastewater process stream in Northwest Ohio. Sci. Total Environ.; 2008; 397, pp. 148-157. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2008.02.042]
75. Laplante, M.; Sabatini, D.M. mTOR signaling in growth control and disease. Cell; 2012; 149, pp. 274-293. [DOI: https://dx.doi.org/10.1016/j.cell.2012.03.017] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22500797]
76. Düvel, K.; Yecies, J.L.; Menon, S.; Raman, P.; Lipovsky, A.I.; Souza, A.L.; Triantafellow, E.; Ma, Q.; Gorski, R.; Cleaver, S. et al. Activation of a metabolic gene regulatory network downstream of mTOR complex 1. Mol. Cell; 2010; 39, pp. 171-183. [DOI: https://dx.doi.org/10.1016/j.molcel.2010.06.022] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20670887]
77. Mooren, F.C.; Krüger, K. Exercise, autophagy, and apoptosis. Prog. Mol. Biol. Transl.; 2015; 135, pp. 407-422.
78. Levine, B.; Kroemer, G. Biological functions of autophagy genes: A disease perspective. Cell; 2019; 176, pp. 11-42. [DOI: https://dx.doi.org/10.1016/j.cell.2018.09.048]
79. Han, J.; Wang, Y. mTORC1 signaling in hepatic lipid metabolism. Protein Cell; 2018; 9, pp. 145-151. [DOI: https://dx.doi.org/10.1007/s13238-017-0409-3]
80. Pfeffer, C.M.; Singh, A.T.K. Apoptosis: A target for anticancer therapy. Int. J. Mol. Sci.; 2018; 19, 448. [DOI: https://dx.doi.org/10.3390/ijms19020448]
81. Perotti, M.; Toddei, F.; Mirabelli, F.; Vairetti, M.; Bellomo, G.; McConkey, D.J.; Orrenius, S. Calcium-dependent DNA fragmentation in human synovial cells exposed to cold shock. FEBS Lett.; 1990; 259, pp. 331-334. [DOI: https://dx.doi.org/10.1016/0014-5793(90)80040-P]
82. Lopez, J.; Tait, S.W.G. Mitochondrial apoptosis: Killing cancer using the enemy within. Brit. J. Cancer; 2015; 112, pp. 957-962. [DOI: https://dx.doi.org/10.1038/bjc.2015.85]
83. Guicciardi, M.E.; Leist, M.; Gores, G.J. Lysosomes in cell death. Oncogene; 2004; 23, pp. 2881-2890. [DOI: https://dx.doi.org/10.1038/sj.onc.1207512] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15077151]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
1 College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China; Yangtze River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Wuhan 430223, China
2 Yangtze River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Wuhan 430223, China
3 College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China