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Article

Comparative Analysis of Skeletal Muscle Metabolites of Fish with Various Rates of Aging

by
Dmitry L. Maslov
*,
Oxana P. Trifonova
,
Anton N. Mikhailov
,
Konstantin V. Zolotarev
,
Kirill V. Nakhod
,
Valeriya I. Nakhod
,
Nataliya F. Belyaeva
,
Marina V. Mikhailova
,
Petr G. Lokhov
and
Alexander I. Archakov
Institute of Biomedical Chemistry, 10 building 8, Pogodinskaya street, Moscow 119121, Russia
*
Author to whom correspondence should be addressed.
Fishes 2019, 4(2), 25; https://0-doi-org.brum.beds.ac.uk/10.3390/fishes4020025
Submission received: 28 December 2018 / Revised: 15 March 2019 / Accepted: 23 March 2019 / Published: 31 March 2019
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Abstract

:
Fish species exhibit great diversity rating of aging (from negligible to rapid), which gives a unique possibility for the discovery of the molecular mechanisms that determine the differences in the rate of aging. A mass spectrometric metabolic profiling of skeletal muscle of fish with various aging rates was carried out by direct injection to a quadrupole time-of-flight mass spectrometer. The first group includes long-lived fish species (pike (Esox Lucius) and sterlet (Acipenser ruthenus); the second group—species with gradual senescence such as that observed in many mammalian species of similar size (zander (Sandra lucioperca) and perch (Perca fluviatilis)) and the third group—species with very short life cycle (chum salmon (Oncorhynchus keta) and pink salmon (Oncorhynchus gorbuscha)). Multivariate analysis of metabolic profiles allowed the detecting of about 80 group-specific features associated with amino acids, lipids, biogenic amines, intermediates of glycolysis, glycogenolysis, and citric acid cycle. Possible roles in the aging process are hypothesized for the biochemical pathways of the metabolites that were altered in the different groups.

1. Introduction

A great range of ages is observed among different animals, including various species of fish. Some fish species (rockfish, sturgeon) has not revealed any age-related decline in their physiological capacity (including reproduction) for several decades of observation. The life cycle of other species (genus nothobranchius) is very short and does not exceed several weeks even in protected environments [1,2]. During the investigation of the aging process in animals, C. Finch has proposed to divide organisms into three categories differing in the rate of aging: negligible (long-lived species), gradual (species whose aging rate is similar to mammal species), and rapid (species with very short life cycle) [3]. According to the Finch’s criteria, the main signs of negligible aging are continuous growth (although slow in most cases), the absence of an age-related increase in mortality rate, the absence of a decline in physiological capacity (including reproductive capacity) and disease resistance [3]. Sterlet, as well as flatfish, beluga, and rockfish, were classified as species with negligible aging [4]. Pike (Esox Lucius) can also be attributed to the same group [5]. Such species as guppy, red panchax, medaka, platyfish, Indian murrel, zander, and perch exhibit gradual senescence, which is typical for most vertebrates [4,6]. The age-related decline of regenerative ability, increasing the probability of physiological damages with advancing age, is an attribute of these species [5].
Finally, the last group (rapid aging) is characterized by rapid senescence with more or less synchronous deterioration of physiological capacity of all organs of an organism. Such species often die after spawning. Lampreys, eels, and Pacific salmonids may be included in this category.
The observable diversity in the lifespan among fish species provides an opportunity for the investigation of mechanisms responsible for dramatic differences in the rate of aging [7]. The long-lived species (negligibly senescent) may be considered as original antiaging models. Investigation of these species may facilitate determination of the pathways that protect effectively against the degenerative process. On the contrary, rapidly senescent species may be considered as models of accelerated aging. Comprehensive research of the species can help identify mechanisms associated with the fast development of age-related pathologies [8,9].
A progressive decrease of muscle mass and strength leading to deterioration of physiological functions of an organism, as well as the development of age-related disorders are one of the most notable factors of aging [10]. Skeletal muscles play a key role in the maintenance of healthy and active lifestyle since they are involved in many essential functions: control of movement and pose, physical function, participation in metabolism (for example, skeletal muscles are critical for the maintenance of glycaemic level), etc. [11,12,13]. Unfortunately, our knowledge about the pathophysiology of the loss of muscle mass and strength is still limited [14].
A metabolomics-based investigation of the skeletal muscle of the three fish groups with different aging rate was performed: the first group consists of biosamples of negligibly senescent species (pike (Esox lucius) and sterlet (Acipenser ruthenus), the second group—biosamples of gradually senescent species (zander (Sander lucioperca) and perch (Perca fluviatilis), and the third one includes biosamples of rapidly senescent species (chum salmon (Oncorhynchus keta), and pink salmon (Oncorhynchus gorbuscha). Comparative analysis of untargeted metabolomics data allows detecting the group-specific features. Subsequent analysis of the selected features enables the removal of artifacts, identify metabolites and detect pathways, in which the metabolites may be involved. An investigation of the contribution of these pathways to the functioning of the organism may provide insights into biological mechanisms related to the processes of maintenance of muscle mass and development of degenerative process [15].

2. Results

The method of Direct-Injection Electrospray Ionization Mass Spectrometry (DI-ESI-MS) was used in the study. An application of an untargeted approach enabled the detection of about 4000 different m/z ions. Multivariate analysis of metabolic data was performed after normalization and the removal of artifacts and outliers. Metabolite ions that were found at least in 70% of samples in each of the compared groups were admitted to the analysis. Principal component analysis of mass spectrometry spectra allowed validating the presence of significant differences in metabolic composition between samples related to different groups (Figure 1). A Kruskal-Wallis H test reconfirmed the results and enabled to detect features, the intensity of which was significantly different (p < 0.05) in the compared groups. It should be noted that no significant gender differences in the metabolic profiles were revealed (data not shown).
About 80 statistical significant features (p < 0.05) were detected by the comparative analysis. Results of the putative identification of the features showed that they are amino acids, biogenic amines, carnitines, intermediates of glycolysis, glycogenolysis, citric acid cycle, and lipid metabolism (Table 1). The identification of the metabolites was carried out by using two orthogonal characteristics (accurate mass and isotopic abundance distribution) that satisfy level 2 (putatively annotated compounds) according to the Metabolomics Standards Initiative (MSI) requirements (http://www.metabolomics-msi.org/). It should be noted that some masses were redundant—they have several candidates. The candidates have identical brutto-formula and, consequently, identical isotope distribution that does not permit proper differentiating by the method of metabolite identification applied in this study. However, these candidates belong to the same compound classes that meet the level 3 of metabolite identification (putatively annotated compound classes) [16]. The further consideration of such metabolites represented the authors’ point of view based on previously published results of identification.
For more specific identification, a tandem mass spectrometry (MS/MS) method was applied. Metabolites of great biological significance (observed mainly in the samples related to negligible and gradual senescence) and whose concentration exceeded the limits of the method were selected for the analysis. Figure 2 shows the fragment list resulting from MS/MS fragmentation of selected metabolite (m/z—162,1150).
Figure 3 shows fragment list resulting from MS/MS fragmentation of selected metabolites (m/z—132,0777 and m/z—132,1024).
Results of metabolites identification using MS/MS analysis are summarized in Table 2.
Thus the results of MS/MS fragmentation confirmed the results of the earlier identification.
The distribution of the relative intensity of the putative metabolites in the compared groups is demonstrated in Figure 4. Careful examination of selected metabolites allowed dividing the major part of them into several groups. The first group may be named as the “group of amino acids”. A high level of some amino acids (valine, leucine, alanine, taurine, and hypotaurine) was observed in the samples related to negligible senescence (Figure 4 metabolites 1, 8, 11, 13, and 14). However, a high level of some of these metabolites was also registered in the samples related to gradual senescence. The second group is the “group of biogenic amines”. A high level of spermidine was observed in the samples related to negligible senescence (Figure 4 metabolite 17). The third group of metabolites may be named as the “group of energy metabolism”. Creatine, creatinine, carnitines, and the intermediates of the citric acid cycle may be associated with the group. A high level of сreatine, creatinine, and L-carnitine is noted in the samples related to negligible senescence (Figure 4 metabolites 3, 4, 12), while the accumulation of medium and long-chain acylcarnitines and the intermediates of the citric acid cycle (fumarate, malate, alpha-ketoglutarate, and citrate) is observed in the samples related to rapid senescence (Figure 4 metabolites 39, 41, 42 48). The fourth group may be named as the “group of intermediates of sugar metabolism”. There was an accumulation of the intermediates of sugar metabolism (maltose, lactate, and pyruvate) in the samples related to rapid senescence (Figure 4 metabolites 33, 34, 63). Finally, the fifth group is the “group of lipid metabolism”. A high level of various lipid intermediates was observed in the samples related to rapid senescence.

3. Discussion

A method of comparative analysis is one of the universal approaches for the detection and identification of key components that are a source of heterogeneity between samples [17]. Fish are the ideal model for investigations of the biochemical basis of the aging process by the approach. The availability of a large number of similar physiological, biochemical, and histological characteristics between fish species enables one to minimize the list of features which should be analyzed to identify metabolites and pathways that are possibly associated with the aging process. Availability of a lot of fish species belonging to different types of aging enable to include several species in each analyzed group allowing to eliminate a various species-specific metabolic perturbations (only group-specific perturbations should be selected for subsequent analysis).
An opportunity to perform large-scale experiments is another one advantage of the experimental model with fish. Most of the fish organs (heart, excretory organs, digestive organs, etc.) are analogous to organs of other vertebrates [18]. This fact provides the possibility of translating the results onto processes in mammals. This study is a follow-up to a previously published investigation of blood metabolic profiles associated with the various ageing rate of fishes [19]. The research was focused on the analysis of metabolic compositions of fish muscle tissues for the identification of distinctive features between compared groups. A direct injection mass spectrometry (DIMS) approach was used in the study. Despite a large number of limitations (ion suppression, inability of isobars separation, etc.), until now DIMS has been a popular approach [20]. High throughput and reproducibility, wide metabolomic coverage (no loss of metabolites due to the application of any separation tools), and short analysis time are advantages of the DIMS method allow the improvement of the reliability and accuracy of the results of subsequent multivariate statistical analysis [20,21].
The largest number of identified metabolites belongs to lipids: a high level of storage lipids (tri- di- and monoacylglycerols, fatty acids, cholesterol) is found in salmonids. Accumulation of distinct phospholipids is detected for in each compared group. There are many of external and internal factors (environment, diet, feeding regime and digestion, etc.) that can influence the fatty acids’ compositions in lipids [22,23]. For example, it was demonstrated that the level of muscle fatty acids composition of the same fish species depends on habitat-independent environmental factors [24,25]. Most likely the revealed differences in abundance of lipids in the compared fish species are the result of the effect of such factors. Salmonids are classified as “fatty” fish species because they have a large amount of storage lipids in muscle compared to other fish species [26,27].
There is very little information about the effects and possible mechanisms of action of lipids on biological processes which can be associated with aging. In some studies related to the aging research, an increase or decrease of the level of distinct lipids with age is noted. Unfortunately, the mechanism of this accumulation or loss of the lipids is not specified.
The intensification of glycogenolysis and glycolysis is the most probable cause of accumulation of metabolites of the “group of intermediates of sugar metabolism” (maltose, lactate, and pyruvate), medium and long-chain acylcarnitines, and the intermediates of the citric acid cycle (fumarate, malate, alpha-ketoglutarate, and citrate) (Figure 4, metabolites 33, 34, 39, 41, 42, 48, and 63), which are observed in the samples of rapid aging fish [28,29,30,31,32,33]. Activation of these processes with a simultaneous decline in the creatine and creatinine levels in salmonids species (Figure 4, metabolites 3 and 12) is probably due to the intensive swimming of fish during the migration for spawning [34]. However, it is possible that the accumulation of the intermediates of glucose is a result of any other factor or the combined effect of several factors. For example, mitochondrial dysfunction leads to similar changes: intracellular accumulation of intermediates of the citric acid cycle, intermediates of glucose metabolism, etc. [32,35,36,37].
Amino acids and biogenic amines, a high level of some of which was observed in negligible senescent and gradually aging species (Figure 4, metabolites 1, 8, 11, 13, and 14), exhibit a wide range of biological effects. Amino acids are one of the key elements of many cellular processes: differentiation and growth of skeletal muscles, osmoregulation, reproduction, immune response, and the precursors of biologically active compounds [38]. They are involved in antioxidant protection (taurine and hypotaurine) and energy metabolism (alanine, valine, and leucine), enhance protein synthesis and inhibit proteolysis (leucine and valine) [39,40,41,42]. A number of studies have noted that an increase in the level of amino acids and biogenic amines leads to a decrease of the development and expression rate of age-related pathologies as well as an extension of the activity period of the musculoskeletal system in mammalia [43]. Most likely, the action of amino acids and biogenic amines in fish muscles is similar. However, the pathways for fish have not been established yet [38].
Based on the observations mentioned above, we can conclude that the capacity of antioxidant protection, the productivity of anabolic processes and, possibly, the effectiveness of energetic metabolism in skeletal muscle tissue samples obtained from negligible senescent and gradually aging species are higher in comparison with similar processes in skeletal muscle tissue samples related to rapid aging. A decrease in the effectiveness of these processes or their damage may lead to a loss of muscle mass and strength with aging [44].

4. Material and Methods

4.1. Muscle Sampling

The samples of young, ready-for-reproduction adults (all of them had visible developing gonads) were used in our work: the first group of pike (N: 12; an average mass of 1.2 ± 0.5 kg and body length—55 ± 7 cm) and sterlet (N: 12; 1.5 ± 0.3 kg; 70 ± 5 cm), the second group of zander (N: 10; 2.2 ± 1.1 kg; 50 ± 14 cm) and perch (N: 8; 0.13 ± 0.07 kg; 23 ± 3 cm), the third group—pink salmon (N: 10; 1.55 ± 0.45 g; 51 ± 3 cm) and chum salmon (N: 10; 3.1 ± 1.3 kg; 63 ± 7cm). The ratio of males to females for each species was approximately 1:1. The age of each fish was determined by analyzing the growth rings on the scales [45]. The salmon samples were obtained from fish caught near Sakhalin Island (Russia); all others were obtained from fish caught in the Uglich Reservoir (Russia) during September 2014 and May–June 2015 (collection permits: no. 69 2014 03 0722, no. 69 2015 03 0683, and no. 69 2016 03 0737, issued by the Federal Agency for Fisheries of the Russian Federation). The study was conducted in accordance with the Declaration of Helsinki.
Samples of muscles (about 0.1 g) were cut off from the spinal area. Visually, the samples did not contain any inclusions of other tissues. In order to better reflect the original metabolic state, the samples were immediately frozen and stored at −80 °C before the analysis [46].

4.2. Extraction Method and Sample Preparation

The extraction methods used in this work were adapted from previously published investigations [47,48] with modifications. Frozen tissue samples were lyophilized overnight. Then, each sample was weighed, homogenized (Bandelin Sonopuls HD 2200, Sigma-Aldrich, St. Louis, MO, USA ), divided into Eppendorf vials and extracted using 20 mL/g (dry mass) of solution ethanol-water (4:1), precooled to 4 °C. After that, the suspension was maintained at 4 °C for 60 min (incubation period) and strongly vortexed for 30 s (10 times), being placed on ice in between. All reagents were of analytical grade and were used without further purification. After centrifugation (15,000 g; 15 min; 4 °C), the supernatant was collected, distributed into Eppendorf vials (Eppendorf, Hamburg, Germany) and stored at −80 °C before analysis. Before mass spectrometry analysis to each aliquot (10 µL) of the supernatant, the fifty volumes of 90% methanol (Fluka, Munich, Germany) with 0.1% formic acid (Fluka, Munich, Germany) was added to obtain the analyzed solution [49].

4.3. Mass Spectrometry Analysis

The samples were analyzed using a hybrid quadrupole time-of-flight mass spectrometer (micrOTOFQ, Bruker Daltonics, Billerica, MA, USA) equipped with electrospray ionization (ESI) source. The mass spectrometer was set up for priority detection of ions with the mass-to-charge ratio (m/z) range from 80 to 1000 and mass accuracy up to 5 parts per million (ppm). ES Tuning Mix (Agilent Technologies, Santa Clara, CA, USA) was used for calibration of the mass spectrometer. The spectra were recorded in the positive ion charge detection mode. The samples were injected into the ESI source using a glass syringe (Hamilton Bonaduz AG, Bonaduz, Switzerland) connected to a syringe injection pump (KD Scientific, Holliston, MA, USA). The flow rate of samples to the ionization source was 180 µL/h, and the samples were injected in a randomized order [49]. More detailed identification of the several selected metabolites was produced through ion fragmentation by collision-induced dissociation (CID) of the ions of interest (precursor ion). Ultra-pure nitrogen was used as collision gas. The fragmentation of the metabolites was performed at different collision energies (10 and 20 eV). The mass spectra were recorded using DataAnalysis version 3.4 (Bruker Daltonics, Germany) to summarize one-minute signals.

4.4. Data Analysis

Data analysis was used for the preliminary peak selection and recalibration of the spectra. The selection parameters were as follows: peak width—5, signal-to-noise ratio—2, relative and absolute threshold intensity—0.05% and 100 respectively. Alignment of mass spectrum peaks, removal of low-informative peaks, and data correction to address ionic inconsistency in samples were performed by using the self-made algorithm in Excel. Two peaks were considered to be related to the same metabolite if the mass difference did not exceed 0.05 Da. All peaks’ intensity and area values were normalized by the internal standard (IS) losartan (C22H23ClN6O, m/z = 423.169) concentration levels.
Analysis of acquired metabolite profiling data was performed by means of Principal Component Analysis (PCA) implemented in the software package ProfileAnalysis version 3.4 (Bruker Daltonics, Germany). Statistically significant differences between the study groups were evaluated using the Kruskal-Wallis H test implemented in the software package Statistica (StatSoft Inc., Tulsa, OK, USA).

4.5. Mass Spectra Peak Identification

The basic approach for identification of the selected peaks with clear isotope patterns consisted in searching of annotated metabolites, which had the closest m/z values to those detected in the spectra [50]. The following reference databases were used: Human Metabolome DataBase (HMDB) (http://www.hmbd.ca), METLIN (http://metlin.scripps.edu), and LIPID MAPS (www.lipidmaps.org). A mass tolerance window was 0.05 Da. Several additional steps were taken to minimize misidentification. Peaks of parental ion and possible adducts of suppositional metabolites in the spectra were found. Next, comparative analysis of isotope patterns of the metabolites or their possible adducts in the spectra with their theoretical isotope patterns was performed [50]. Theoretical isotope patterns for each of the metabolites and adducts were produced using Isotope Pattern Calculator (Bruker Daltonics, Germany). Tandem mass spectrometry (MS/MS) was applied for more specific identification of several metabolites. In this case, identification was performed by comparing the MS/MS spectra obtained at different collision energies in positive ionization modes with the reference MS/MS spectra from the public metabolite database (METLIN). A mass tolerance window, in this case, was 0.005 Da.

5. Conclusions

In summary, a wide range of metabolites that belong to different classes was detected in muscles of fish with different lifespan. Applications of metabolic profiling in combination with statistical analyses allowed to identify the group-specific features. The obtained results made it possible to suggest the biochemical pathways involving the identified metabolites and, perhaps, associated with the rate of aging. Utilization of these results may enhance our knowledge of aging processes, facilitate the development of new rational approaches to prevent or even delay age-associated alterations in muscle, and hence improve health lifespan.

Author Contributions

Conceptualization, D.L.M., M.V.M., P.G.L. and A.I.A.; methodology, D.L.M., M.V.M. and P.G.L.; software, D.L.M. and O.P.T. validation, D.L.M., K.V.Z. and M.V.M.; formal analysis, D.L.M. and O.P.T.; investigation, D.L.M., O.P.T., A.N.M., K.V.Z., K.V.N., V.I.N. and N.F.B.; resources, D.L.M., A.N.M., K.V.Z., K.V.N., V.I.N. and N.F.B.; data curation, D.L.M. and O.P.T.; writing—original draft preparation, D.L.M.; writing—review and editing, D.L.M., M.V.M. and P.G.L.; visualization, D.L.M.; supervision, D.L.M., M.V.M., P.G.L. and A.I.A.; project administration, D.L.M., M.V.M. and P.G.L.; funding acquisition, M.V.M., P.G.L. and A.I.A.

Funding

The work was done within the framework of the State Academies of Sciences Fundamental Scientific Research Program for 2013–2020. Mass spectrometry measurements were performed using the equipment of “Human Proteome” Core Facilities of the Institute of Biomedical Chemistry (Russia).

Conflicts of Interest

The authors declare no conflict of interest.

Ethical Approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

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Fishes 04 00025 g001 550
Figure 1. The Principal Component Analysis (PCA) score plot of mass spectrometry spectra illustrates the clustering of samples according to attributed group membership (negligible (×), gradual (Δ), and rapid aging (O)), with more than 80% of total variance (the first two components). The clustering displays a difference in the metabolic composition between the compared groups.
Figure 1. The Principal Component Analysis (PCA) score plot of mass spectrometry spectra illustrates the clustering of samples according to attributed group membership (negligible (×), gradual (Δ), and rapid aging (O)), with more than 80% of total variance (the first two components). The clustering displays a difference in the metabolic composition between the compared groups.
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Figure 2. The results of tandem mass spectrometry (MS/MS) fragmentation of selected metabolites (m/z—162,1150). MS/MS spectra obtained in positive ionization mode. Collision energy—10 eV. Reference m/z value of fragments of l-carnitine, chemical formula and ion type (taken from the public metabolite database) are also indicated in the figure.
Figure 2. The results of tandem mass spectrometry (MS/MS) fragmentation of selected metabolites (m/z—162,1150). MS/MS spectra obtained in positive ionization mode. Collision energy—10 eV. Reference m/z value of fragments of l-carnitine, chemical formula and ion type (taken from the public metabolite database) are also indicated in the figure.
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Figure 3. The results of MS/MS fragmentation of selected metabolites (m/z—132,0777 and m/z—132,1024). MS/MS spectra obtained in positive ionization mode. Collision energy—20 eV. Reference m/z value of fragments of leucine, creatine, chemical formula and ion type (taken from the public metabolite database) are also indicated in the figure.
Figure 3. The results of MS/MS fragmentation of selected metabolites (m/z—132,0777 and m/z—132,1024). MS/MS spectra obtained in positive ionization mode. Collision energy—20 eV. Reference m/z value of fragments of leucine, creatine, chemical formula and ion type (taken from the public metabolite database) are also indicated in the figure.
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Figure 4. Results of the univariate statistical analysis identified metabolites with statistically significant differences among groups (negligible—(×), gradual—(◊) and rapid senescence—(O)). A.-metabolites related to a “group of amino acids” and a “group of biogenic amines” (alanine, valine, leucine, taurine, hypotaurine, and spermidine); B.-metabolites related to a “group of energy metabolism” (creatine, creatinine, carnitines, fumarate, malate, alpha-ketoglutarate, and citrate); C.-metabolites related to a “group of intermediates of sugar metabolism” (maltose, lactate, and pyruvate). The scatter plots show the intensity (relative units) of identified metabolites in the three compared groups. (**—p < 0.01; *—p < 0.05). Numbers are corresponded to ordinal metabolites numbers indicated in Table 1.
Figure 4. Results of the univariate statistical analysis identified metabolites with statistically significant differences among groups (negligible—(×), gradual—(◊) and rapid senescence—(O)). A.-metabolites related to a “group of amino acids” and a “group of biogenic amines” (alanine, valine, leucine, taurine, hypotaurine, and spermidine); B.-metabolites related to a “group of energy metabolism” (creatine, creatinine, carnitines, fumarate, malate, alpha-ketoglutarate, and citrate); C.-metabolites related to a “group of intermediates of sugar metabolism” (maltose, lactate, and pyruvate). The scatter plots show the intensity (relative units) of identified metabolites in the three compared groups. (**—p < 0.01; *—p < 0.05). Numbers are corresponded to ordinal metabolites numbers indicated in Table 1.
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Table
Table 1. Distinctive m/z features and putatively identified skeletal muscle metabolites typical for fish species with different lifespan.
Table 1. Distinctive m/z features and putatively identified skeletal muscle metabolites typical for fish species with different lifespan.
Mass of Ion
№MetaboliteHMDB IDMeasured (m/z)Calculated (m/z)Monoisotopic Mol Weight (Da)Ion FormElemental Composition
metabolites a high level of which was observed in the samples relating to negligible
1hypotaurineHMDB00965110,0175110,027110,0275M+HC2H7NO2S
2creatinineHMDB00562114,0634114,0661113,0589M+HC4H7N3O
3creatinineHMDB00562136,0027136,0481113,0589M+NaC4H7N3O
4L-carnitineHMDB00062162,1123162,1124161,10519M+HC7H15NO3
5n/a---635,3064------------
6PC *HMDB08531844,6487844,679843,6717M+HC48H94NO8P
metabolites a high level of which was observed in the samples relating to negligible and gradual
7alanineHMDB0016190,051890,054989,04767M+HC3H7NO2
8alanineHMDB00161112,0307112,036889,04767M+NaC3H7NO2
9valineHMDB00883118,0851118,0862117,0789M+HC5H11NO2
10sarcosineHMDB00271128,0106128,010889,04767M+KC3H7NO2
11taurineHMDB00251126,0186126,0219125,0146M+HC2H7NO3S
12creatineHMDB00064132,0764132,0767131,0694M+HC4H9N3O2
13leucineHMDB00687132,1024132,1019131,0946M+HC6H13NO2
14valineHMDB00883140,0617140,0681117,0789M+NaC5H11NO2
15taurineHMDB00251147,9842148,0038125,0146M+NaC2H7NO3S
16creatineHMDB00064154,0571154,0586131,0694M+NaC4H9N3O2
17spermidineHMDB01257146,1466146,1651145,1578M+HC7H19N3
18maleylacetateHMDB60348159,0280159,0288158,0215M+HC6H6O5
19leucineHMDB00687170,0253170,0577131,0946M+KC6H13NO2
20maleylacetateHMDB60348196,9787196,9847158,0215M+KC6H6O5
21n/a---713,4983------------
metabolites a high level of which was observed in the samples relating to gradual
22itaconic acidHMDB02092152,9764153,0158130,0266M+NaC5H6O4
23n/a---771,6006------------
24PC *HMDB08136802,5179802,5357779,5465M+NaC44H78NO8P
25PC *HMDB08138804,5363804,5513781,5621M+NaC44H80NO8P
26PC *HMDB08136822,5332822,5410783,5778M+KC44H82NO8P
27PC *HMDB08589826,6635826,6684825,6611M+HC48H92NO7P
28PC *HMDB08304832,5702832,5827809,5934M+NaC46H84NO8P
29PC *HMDB08272834,5845834,5983811,6091M+NaC46H86NO8P
30PC *HMDB08467844,5150844,5253805,5621M+KC46H80NO8P
metabolites a high level of which was observed in the samples relating to rapid
31n/a---100,1074------------
32serineHMDB00187106,0391106,0498105,0425M+HC3H7NO3
33pyruvateHMDB00243110,996111,005388,01604M+NaC3H4O3
34lactic acidHMDB00190113,0268113,020990,03169M+NaC3H6O3
35cysteineHMDB00574122,0211122,0270121,0197M+HC3H7NO2S
36cholineHMDB00097127,0841127,0967104,1075M+NaC5H14NO
37lactic acidHMDB00190128,9881128,994890,03169M+KC3H6O3
38n/a---137,1283------------
39fumaric acidHMDB00134138,9945139,0002116,0109M+NaC4H4O4
40niacinamideHMDB01406145,0250145,0372122,048M+NaC6H6N2O
41malateHMDB00156156,998157,0107134,0215M+NaC4H6O5
42α-ketoglutarateHMDB00208168,9989169,0107146,0215M+NaC5H6O5
43serinyl-alanineHMDB29032177,0656177,0869176,0797M+HC6H12N2O4
44methylenesuccinic acidHMDB59762180,9791180,9898142,0266M+KC6H6O4
45α-ketoglutarateHMDB00208184,9788184,9846146,0215M+KC5H6O5
46threoninyl-alanineHMDB29054191,0972191,1026190,0953M+HC7H14N2O4
47serinyl-alanineHMDB29032190,0210190,0689176,0797M+NaC6H12N2O4
48citric acidHMDB00094215,0120215,0162192,0270M+NaC6H8O7
49n/a---227,1875------------
50tridecanoic acidHMDB00910237,1446237,1825214,1932M+NaC13H26O2
51palmitoleic acidHMDB03229279,2001279,2294256,2402M+NaC16H30O2
52n/a---285,2529------------
53linolenic acidHMDB013301,1796301,2138278,2245M+NaC18H30O2
54linoleic acidHMDB00673303,2024303,2294280,2402M+NaC18H32O2
55oleic acidHMDB020305,2036305,2451282,2558M+NaC18H34O2
56linolenic acidHMDB013317,1567317,1877278,2245M+KC18H30O2
57linoleic acidHMDB00673319,1922319,2033280,2402M+KC18H32O2
58eicosapentaenoic acidHMDB01999325,2039325,2138302,2245M+NaC20H30O2
59eicosapentaenoic acidHMDB01999341,1619341,1877302,2245M+KC20H30O2
60eicosadienoic acidHMDB05060347,2045347,2346308,2715M+KC20H36O2
61docosahexaenoic acidHMDB00021351,2073351,2294328,2402M+NaC22H32O2
62docosatrienoic acidHMDB02823357,2388357,2764334,2871M+NaC22H38O2
63d-maltoseHMDB00163365,0830365,1054342,1162M+NaC12H22O11
64MG *HMDB11539375,2064375,2505352,2613M+NaC21H36O4
65MG *HMDB00115379,2473379,2818356,2926M+NaC21H40O4
66tetradecenoylcarnitineHMDB02014392,2518392,2771369,2879M+NaC21H39NO4
67MG *HMDB11547419,2218419,2558380,2926M+KC23H40O4
68hydroxycholesterolHMDB02103425,3057425,339402,3497M+NaC27H46O2
69MG *HMDB11551437,3341437,3601414,3709M+NaC25H50O4
70LPA *HMDB07854439,2501439,2819438,2746M+HC21H43O7P
71n/a---445,0474------------
72PA *HMDB11144447,2502447,2845424,2953M+NaC21H45O6P
73LPA *HMDB07855459,2192459,2482436,2589M+NaC21H41O7P
74LysoPC *HMDB10379468,2750468,3084467,3011M+HC22H46NO7P
75LysoPC *HMDB10383494,3033494,3241493,3168M+HC24H48NO7P
76n/a---538,4796------------
77n/a---551,3859------------
78DG *HMDB56009591,4606591,4959568,5066M+NaC35H68O5
79DG *HMDB56010619,4817619,5272596,5379M+NaC37H72O5
80n/a---647,3752------------
81DG *HMDB56204655,4883655,5272632,5379M+NaC40H72O5
82DG *HMDB56298663,4551663,4959640,5066M+NaC41H68O5
83DG *HMDB56037675,5749675,5898652,6005M+NaC41H80O5
84DG *HMDB07430693,4931693,5428670,5536M+NaC43H74O5
85PC *HMDB08519892,6545892,6790891,6717M+HC52H94NO8P
Metabolites were annotated by library search (HMDB), accurate mass and isotopic abundance distribution; m/z—mass-to-charge ratio; n/a—not assigned; DG—diacylglycerol; MG—monoacylglycerol; LPA—lysophosphatidic acid; PA—phosphatidic acid; LysoPC –lysophospholipid; PC—phosphatidylcholine; *—redundant masses with several candidates.
Table
Table 2. Identification of selected metabolites by MS/MS fragmentation.
Table 2. Identification of selected metabolites by MS/MS fragmentation.
MetabolitePrecursor IonProduct Ion
Registered
m/z, Da		Calculated
m/z, Da		Chemical FormulaIon TypeRegistered
m/z, Da		Reference
m/z, Da		Chemical FormulaIon Type
leucine132,1024132,1019C6H13NO2[M + H]+86,09786,0969 *C5H11N[M]+
---44,0499 *C2H5N[M + H]+
---43,0546 *C3H6[M + H]+
creatine132,0777132,0764C4H9N3O2[M + H]+114,065114,0664 *C4H6N3O[M + H]+
90,054390,0552 *C3H7NO2[M + H]+
L-carnitine162,1126162,1125C7H15NO3[M + H]+103,0402103,039 *C4H7O3[M]+
102,0914102,0913 *C5H12NO[M]+
85,031285,0284 *C4H4O2[M + H]+
Identification was performed by comparing detected m/z values of fragments (resulting from MS/MS fragmentation of selected metabolites) to corresponding m/z value of the reference fragments of leucine, creatine, and l-carnitine from the public metabolite database; m/z—mass-to-charge ratio. A mass tolerance window—0,005 Da. *—the reference results of MS/MS fragmentation were taken from public metabolite database METLIN.

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Maslov, D.L.; Trifonova, O.P.; Mikhailov, A.N.; Zolotarev, K.V.; Nakhod, K.V.; Nakhod, V.I.; Belyaeva, N.F.; Mikhailova, M.V.; Lokhov, P.G.; Archakov, A.I. Comparative Analysis of Skeletal Muscle Metabolites of Fish with Various Rates of Aging. Fishes 2019, 4, 25. https://0-doi-org.brum.beds.ac.uk/10.3390/fishes4020025

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Maslov DL, Trifonova OP, Mikhailov AN, Zolotarev KV, Nakhod KV, Nakhod VI, Belyaeva NF, Mikhailova MV, Lokhov PG, Archakov AI. Comparative Analysis of Skeletal Muscle Metabolites of Fish with Various Rates of Aging. Fishes. 2019; 4(2):25. https://0-doi-org.brum.beds.ac.uk/10.3390/fishes4020025

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Maslov, Dmitry L., Oxana P. Trifonova, Anton N. Mikhailov, Konstantin V. Zolotarev, Kirill V. Nakhod, Valeriya I. Nakhod, Nataliya F. Belyaeva, Marina V. Mikhailova, Petr G. Lokhov, and Alexander I. Archakov. 2019. "Comparative Analysis of Skeletal Muscle Metabolites of Fish with Various Rates of Aging" Fishes 4, no. 2: 25. https://0-doi-org.brum.beds.ac.uk/10.3390/fishes4020025

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