Abstract
Given that the extent to which genetics alters the metabolomic profile
of tissues is still poorly understood, the current study aimed to
characterize and investigate the metabolite profiles of brain, liver,
kidney and skeletal muscle of two common mouse inbred strains (BALB/c,
C57BL/6) and one outbred stock (CD1) for strain-specific differences.
Male mice (n = 15) at the age of 12 weeks were used: BALB/c (n = 5),
C57BL/6 (n = 5) and CD1 (n = 5). Solid phase microextraction (SPME) was
applied for the extraction of analytes from the tissues. SPME fibers
(approximately 0.2 mm in diameter) coated with a biocompatible sorbent
(4 mm length of hydrophilic-lipophilic balanced particles) were
inserted into each organ immediately after euthanasia. Samples were
analyzed using liquid chromatography coupled to a Q-Exactive Focus
Orbitrap mass spectrometer. Distinct interstrain differences in the
metabolomic patterns of brain and liver tissue were revealed. The
metabolome of kidney and muscle tissue in BALB/c mice differed greatly
from C57BL/6 and CD1 strains. The main compounds differentiating all
the targeted organs were alpha-amino acids, purine nucleotides and
fatty acid esters. The results of the study indicate that the baseline
metabolome of organs, as well as different metabolic pathways, vary
widely among general-purpose models of laboratory mice commonly used in
biomedical research.
Keywords: mouse strain, tissue, untargeted metabolomics, metabolomic
profile, solid-phase microextraction
1. Introduction
In view of the resemblance between mice (Mus musculus) and humans on
genetic, anatomical, physiological, and pathophysiological levels, the
mouse is nowadays considered to be the most common model in biomedical
research, as well as the most published and well-characterized
[[34]1,[35]2]. According to the latest report concerning the number of
animals used for scientific purposes in the Member States of the
European Union in 2017, mice constituted 61% of all animals used in
studies [[36]3]. There currently exists hundreds of established inbred,
outbred, and transgenic mouse strains with defined genetic backgrounds
and unique features, such as coat color, behavior, metabolism,
fertility, immune function, and other physiological traits [[37]4].
Among the most commonly used strains, the inbred C57BL/6 and BALB/c
lines, as well as the CD1 outbred stock, are particularly predominant
in research. As these strains are considered to be general-purpose
models, they are extensively and often interchangeably used in many
different disciplines of biomedical research. The first two of the
above-mentioned strains are considered ‘classical’ inbred strains,
signifying that the animals are genetically homogeneous and individual
mice within a strain are identical clones of their parents and siblings
[[38]5]. Generally speaking, the use of inbred strains has become
standard for research in most areas of mouse biology, as well as for
basic and preclinical investigations. Currently, the most common
strain, C57BL6, is used in a wide variety of research areas, including
cardiovascular and developmental biology, diabetes and obesity,
genetics, immunology, neurobiology, and sensorineural research. BALB/c
mice are currently among the five most widely used inbred strains in
biomedical applications, and a particular favorite in immunology and
infectious disease research [[39]6]. CD1 mice, in contrast to the two
strains mentioned above, is an outbred stock that is bred specifically
to maximize genetic diversity and heterozygosity within a population.
The CD1 line is a general multipurpose model that can be used in fields
such as toxicology (safety and efficacy testing), aging, and oncology
[[40]5,[41]7].
Metabolomics, or metabolic phenotyping, encompasses the analysis of all
low-molecular-weight (< 1 kDa) components in a biological sample, and
it is generally considered to be a field of research complementary to
proteomics, genomics, and transcriptomics. The metabolome, representing
the total number of metabolites within a given biological system, may
consist of metabolites solely under endogenous control, and may also
involve those originating from exogenous sources [[42]8,[43]9]. Sets of
small metabolites attainable in metabolomics are closely related to the
phenotypes of living organisms and provide information on biochemical
activities by reflecting the substrates and products of cellular
metabolism [[44]10,[45]11,[46]12]. The metabolome is dynamic and
susceptible to many factors such as environment, genetic modifications,
changes in gut microflora and altered kinetic activity of enzymes, and
can thus provide insights into current cell status as well as
describing an actual health condition of the system being studied
[[47]13]. One of the analytical chemistry strategies applied in
metabolomics is to carry out untargeted analysis of the metabolism so
as to investigate the system on a global, relatively unbiased scale,
thus aiming to measure the broadest range of metabolites present in an
extracted sample without a priori knowledge of the metabolome
[[48]14,[49]15]. Owing to its high sensitivity and high throughput,
this method has gained wide attention as a method for profiling
endogenous metabolites. Untargeted tissue metabolomics in particular
can provide valuable insights into the physiological characteristics of
the body and greatly enhance our understanding of the biochemistry and
metabolism of biological systems [[50]12,[51]16]. However, successful
characterization of the metabolome of tissue is often burdened by
challenging steps such as tissue collection, quenching of the
metabolism, homogenization, and metabolite extraction [[52]17,[53]18].
Solid phase microextraction (SPME) has been proven to be a powerful,
low invasive tool for tissue analysis in untargeted metabolomics
studies [[54]19], overcoming all the challenges mentioned above while
successfully capturing unstable and short-lived metabolites often
undetectable via traditional methods. SPME is a relatively young sample
preparation method with unique features that enable its successful
application in animal studies as an alternative to standard protocols
[[55]20,[56]21]. Choices regarding pretreatment methodology play an
extremely important role in metabolomic studies since sample
pretreatment may affect not only the molecular features available, but
also the biological interpretation of the obtained chromatographic data
[[57]17]. SPME technology uses special fibers coated with biocompatible
sorbents (thin film of polymer) that can be inserted directly into the
tissue (brain, muscle, liver, lungs, etc.) in order to extract small
molecules in amounts proportional to their biologically active unbound
concentrations. SPME has a number of advantages, including simplicity,
high sensitivity and a relatively low invasive nature. Moreover, it is
a non-exhaustive sample preparation procedure based on chemical biopsy
that combines sampling, sample preparation, rapid metabolism quenching,
and extraction into a single step [[58]20,[59]21,[60]22,[61]23,[62]24].
Analysis of tissue as a primary site of any dysregulation has been one
of the main goals of biomedical experiments using animal models.
Therefore, the aim of the present study was to compare the tissue
metabolome of selected organs (liver, kidney, brain, and thigh muscle)
in the three mouse strains commonly used in biomedical research:
C57BL/6, BALB/c and CD1. The outbred stock (CD1) was chosen for this
study to find out to what extent an outbred line differs in its
metabolome compared to inbred strains. We expect that having an insight
into the metabolomic profile of different organs in intact animals will
give us an opportunity to find out how much these three popular strains
differ among each other in terms of the basic metabolic processes
occurring in the body.
2. Results
[63]Figure 1 presents an ion map of molecular features after a data
filtration step, whereas an ion map analysis of all molecular features
detected in mice tissues is presented in [64]Figure S1.
Figure 1.
[65]Figure 1
[66]Open in a new tab
Plot representing the distribution of molecular weight versus retention
time (RT) of filtrated features obtained in LC-MS analysis,
electrospray ionization in positive ion mode (ESI+). Details of
detected features are presented in [67]Table S1.
Principal component analysis (PCA) was used to confirm the quality of
instrumental analysis ([68]Figure S2) and investigate the differences
in the metabolic profiles of organs for all (673) detected features
among C57BL/6, BALB/c and CD1 mice. The two-dimensional score plots
(PC1 vs. PC2) presented in [69]Figure 2 revealed major differences in
metabolomic patterns of brain ([70]Figure 2a) and liver ([71]Figure 2b)
tissue between the examined strains. The metabolome of kidney tissue in
BALB/c mice distinctly separated from C57BL/6 and CD1 mice, whereas
data points in the score plots for C57BL/6 and CD1 mice had mostly
overlapping distributions ([72]Figure 2c). Likewise, for muscle tissue,
the BALB/c strain separated clearly from C57BL/6 and CD1 mice, whereas
distribution differences between C57BL6 and CD1 mice were not so
evident ([73]Figure 2d). Clusters were noticeably larger for BALB/c
mice in kidney, liver, and muscle tissue, but similar for brain tissue
among all examined strains. Additionally, three-dimensional (3D) PCA
score plots (representing relationships between PC1, PC2, and PC3) are
shown in [74]Figure S3. On the whole, 3D scatter plots revealed similar
cluster separations to those of two-dimensional plots. Clear separation
of strains within the brain and liver tissues presented in 2D plots was
confirmed by three-dimensional visualization. Moreover, many more
scattered points for BALB/c compared to the other two mouse strains was
also observed.
Figure 2.
[75]Figure 2
[76]Open in a new tab
Two-dimensional principal component analysis (PCA) of untargeted
metabolomics data of tissues collected from three different mouse
strains. Examined strains included: BALB/c (red), C57BL/6 (green), and
CD1 (blue). Tissues analyzed included: brain (a), liver (b), kidney
(c), and muscle (d).
Identification of detected features was performed on Compound
Discoverer 2.1 software, whereas classification of putative metabolites
on the basis of their chemical taxonomy was done with the use of Human
Metabolome Database HMDB. Selected compounds belonging to a variety of
metabolite classes ([77]Table 1) were subjected to analysis of variance
(ANOVA), revealing that most of the presented metabolites
differentiated (p < 0.05) studied strains within a given organ
([78]Table 1). Metabolites differentiating all the examined tissues
were found to predominantly belong to alpha-amino acids and
derivatives, purine nucleotides and fatty acid ester groups. In
addition, metabolomic profiles of brain tissue differed between strains
in the levels of metabolites belonging to purine derivatives, purine
and pyrimidine nucleosides and derivatives, hydroxy fatty acids and
fatty amides, as well as alcohols and polyols. Liver tissue profiles
differentiated with respect to levels of purine derivatives, purine
nucleosides, ceramides, benzoic acids and derivatives, as well as
imidazoles. Benzoic acids and derivatives, as well as
N-acyl-alpha-amino acids, differentiated kidney tissues whereas
skeletal muscle profiles differed in N-acyl-alpha-amino acids, purine
derivatives and alcohol and polyol levels.
Table 1.
Chemical taxonomy of selected annotated features present in examined
tissues of BALB/c, C67BL/6 and CD1 mice.
Organ Class Compound Molecular Weight Retention Time (min) p-Value
(ANOVA)
Brain Alpha-amino acids and derivatives Proline 115.0636 1.80 0.0019
Valine 117.0792 1.28 0.6876
Asparagine 132.0536 1.19 0.0000
Pyroglutamic acid 129.0427 1.22 0.0104
Cystine 240.0237 1.17 0.0441
N-acetylaspartic acid 175.0481 2.12 0.0005
Tetrahydrodipicolinate 171.0532 1.36 0.0145
N-acyl-alpha-amino acids N-acetylvaline 159.0896 7.69 0.3731
Purine derivatives Xanthine 152.0334 3.23 0.0065
Purine nucleotides Adenosine monophosphate (AMP) 347.0629 1.34 0.0063
Purine nucleosides Inosine 268.0807 6.68 0.0070
8-hydroxydeoxyguanosine 283.0916 6.93 0.0005
Pyrimidine derivatives Uracil 112.0276 1.34 0.0091
Pyrimidine nucleosides 2’-deoxycytidine 227.0905 7.05 0.0002
Fatty acid esters 2-methylbutyrylcarnitine 245.1627 17.87 0.0277
Hydroxy fatty acids Mevalonic acid 148.0737 1.33 0.0483
Fatty amides Oleamide 281.2718 20.64 0.0079
Alcohols and polyols Pantothenic acid 219.1107 7.08 0.0273
Liver Alpha-amino acids and derivatives Proline 115.0636 1.80 0.0005
Valine 117.0792 1.28 0.0466
Asparagine 132.0536 1.19 0.0190
Pyroglutamic acid 129.0427 1.22 0.0083
Tetrahydrodipicolinate 171.0532 1.36 0.0185
N-acetylaspartic acid 175.0481 2.12 0.5917
N-acyl-alpha-amino acids N-acetylvaline 159.0896 7.69 0.3605
Purine derivatives Xanthine 152.0334 3.23 0.6444
5-hydroxyisourate 184.0233 1.34 0.0004
Purine nucleotides Adenosine monophosphate (AMP) 347.0629 1.34 0.0490
Purine nucleosides Inosine 268.0807 6.68 0.0120
8-hydroxydeoxyguanosine 283.0916 6.93 0.0033
Fatty acid esters 2-methylbutyrylcarnitine 245.1627 17.87 0.0235
Ethyl eicosapentaenoic acid 330.2557 21.65 0.0148
Ceramides Ceramide (d40:1) 621.6063 26.30 0.0175
Benzoic acids and derivatives 2-aminobenzoic acid 137.0476 1.33 0.0048
Imidazoles Allantoin 158.0439 1.15 0.0442
Kidney Alpha-amino acids and derivatives Proline 115.0636 1.80 0.0069
Valine 117.0792 1.28 0.0446
Asparagine 132.0536 1.19 0.7313
Pyroglutamic acid 129.0427 1.22 0.4256
N-acetylaspartic acid 175.0481 2.12 0.0080
N-acyl-alpha-amino acids N-acetylvaline 159.0896 7.69 0.0000
Purine derivatives Xanthine 152.0334 3.23 0.1554
Purine nucleotides Adenosine monophosphate (AMP) 347.0629 1.34 0.0176
Fatty acid esters 2-methylbutyrylcarnitine 245.1627 17.87 0.0003
Ethyl eicosapentaenoic acid 330.2557 21.65 0.0066
Benzoic acids and derivatives 2-aminobenzoic acid 137.0476 1.33 0.0067
Muscle Alpha-amino acids and derivatives Proline 115.0636 1.80 0.0015
Valine 117.0792 1.28 0.3540
Asparagine 132.0536 1.19 0.0236
Pyroglutamic acid 129.0427 1.22 0.0206
N-acetylaspartic acid 175.0481 2.12 0.3746
N-acyl-alpha-amino acids N-acetylvaline 159.0896 7.69 0.6755
N-tridecanoylglycine 271.2147 22.52 0.0407
Purine derivatives Xanthine 152.0334 3.23 0.0635
5-hydroxyisourate 184.0233 1.34 0.0038
Purine nucleotides Adenosine monophosphate (AMP) 347.0629 1.34 0.1549
Fatty acid esters 2-methylbutyrylcarnitine 245.1627 17.87 0.0051
Alcohols and polyols Pantothenic acid 219.1107 7.08 0.0305
[79]Open in a new tab
[80]Table 1 also contains compounds that did not differentiate the
targeted organs (p > 0.05) but were introduced for further comparative
analysis. [81]Figure 3 presents the differences in the levels of
metabolites that were common among all tissues.
Figure 3.
[82]Figure 3
[83]Open in a new tab
Levels of selected metabolites present in brain, kidney, liver and
muscle tissues of BALB/c, C57BL/6 and CD1 mice. Data is presented as
mean ± standard error of the mean (bar:
[MATH: x¯ :MATH]
; whisker- standard error of
[MATH: x¯ :MATH]
); a, b: bars with different letters differ significantly at p < 0.05
(post-hoc Tukey HSD test); MW: molecular weight.
The selected metabolites listed in [84]Table 1 were additionally
subjected to the partial least squares discriminant analysis (PLS-DA).
Results are presented in [85]Figure 4 (scores plots), [86]Figure S4
(loading plots) and [87]Table S2 (validation metrics).
Figure 4.
[88]Figure 4
[89]Open in a new tab
Score plots (PLS-DA) of selected metabolites in tissues collected from
three different mouse strains. Examined strains included: BALB/c (red),
C57BL/6 (green), and CD1 (blue). Tissues analyzed included: brain (a),
liver (b), kidney (c), and muscle (d).
The PLS-DA method was used to more specifically model differences in
the metabolome profiles of the targeted tissues of the compared strains
of mice. Each model was validated (venetian blinds cross-validation)
and refined using a permutation test (number of permutations = 100).
This statistical analysis refined the number of features, yielding an
optimal set of compounds from the selected metabolites (presented in
[90]Table 1) that successfully differentiated strains within each
organ. For brain and liver tissues, the distinctions observed among
BALB/c, C57BL/6 and CD1 mice were mainly similar to the results
obtained in the PCA; however, for kidney and muscle, the separation
between groups was much cleaner, as typically expected from supervised
chemometric methods.
A metabolic pathway analysis was carried out to investigate the key
biochemical pathways of the selected metabolites ([91]Table 1). The
results from the pathway analysis are shown graphically in [92]Figure
5, while the compounds involved in the respective metabolic pathways
are presented in [93]Table S3. The analysis uncovered a total of
fourteen metabolic pathways mainly involved in the metabolism of amino
acids (beta-alanine, valine, leucine, isoleucine, cysteine, methionine,
tryptophan, arginine, proline, asparagine, glutamine), the biosynthesis
of the aminoacyl-tRNA, terpenoid backbone, pantothenate and CoA, as
well as the metabolism of glutathione, sphingolipids, pyrimidine, and
purine.
Figure 5.
[94]Figure 5
[95]Open in a new tab
Pathway analysis of compounds differentiating examined mice strains.
The Y-axis represents log p-values obtained from the pathway enrichment
analysis. The X-axis represents pathway impact values obtained from
pathway topology analysis. Node colors and radii are based on p-values
and pathway impact values, respectively.
3. Discussion
Mammalian tissues, representing the sites of cellular metabolism, are a
main target for metabolomics experiments, which can give us a
‘snapshot’ of a given tissue’s temporary state when properly performed
[[96]25]. Considering that the extent to which genetics alters
metabolism at the tissue level is still poorly understood, we conducted
this study to find out whether two common mouse inbred strains (BALB/c,
C57BL/6) and an outbred stock (CD1) are characterized by peculiar,
strain-specific metabolite profiles, considering the selected organs
(brain, liver, kidney and skeletal muscle) as optimal sites of
extraction given their predominant use in research. The chosen lines of
mice are general multipurpose models that have been used
interchangeably for different scientific purposes. This particularly
applies to the two inbred strains (BALB/c and C57BL/6) selected in this
study. Inbred mice are defined as genetically identical within the
strain and are used in studies in which isogenicity and homozygosity in
the test population are desired. Conversely, in an outbred mouse stock
(CD1), each animal is genetically unique and phenotypic variation in
outbred stocks is usually greater than that of inbred strains due to
both genetic and non-genetic factors [[97]5].
Bearing in mind the fact that an individual’s metabolome is sensitive
to many internal and external variables, including age, gender, diet,
environment, time of day, and even one’s own genetics [[98]12], the
present study was performed on animals of the same age and sex that
were kept in standard environmental conditions. Furthermore, we used
intact mice (no experimental factors were applied) so we could compare
the metabolomic profile of mouse organs under physiological conditions.
The SPME technique was utilized as a sampling tool as it has been
proven to be well suited for metabolomic analysis of tissue
[[99]19,[100]20,[101]21]. SPME additionally enabled extractions
immediately after organ collection in our experiments, which is
particularly desirable in rodent studies given that the metabolomic
profile of tissues changes rapidly after cessation of blood circulation
[[102]25].
The PCA clearly revealed distinct differences in the entire metabolome
of the brain and liver among all examined strains. The kidney and
muscle metabolomic profiles of BALB/c mice distinctly varied from
C57BL/6 and CD1, whereas these profiles in C57BL/6 and CD1 strains
were, to some extent, similar. It should be stressed that the size of
the clusters was larger for BALB/c in liver, kidney and muscle tissues,
indicating greater variability in the metabolome, whereas in brain
tissue, cluster size was similar for all the examined strains. In a
previous study aimed at profiling metabolites in brain, heart, kidney,
and liver tissues of 26 mammalian species representing ten taxonomical
orders, it was suggested that brain metabolites are the most conserved
among the examined organs, and have evolved largely according to the
phylogeny. In contrast, the metabolites of other examined organs
diverged to a much greater extent, possibly due to stronger
environmental influences or other selection pressures [[103]26].
Previous reports [[104]27,[105]28] clearly indicate that genetic
background has profound effects on the overall metabolite profiles of
murine tissues, a factor that was confirmed in our experiment,
especially for the brain and liver. It is also worth mentioning that
significant phenotypic differences covering a number of physiological,
biochemical, and neurobehavioral systems have been previously
identified, even between very close mouse strains such as C57BL/6J and
C57BL/6N [[106]27].
In our work, amino acids and derivatives turned out to be the main
group of compounds differentiating strains within individual organs.
Considering brain tissue, the level of proline was significantly higher
in C57BL/6 and CD1 mice compared to BALB/c. It must be stressed that a
similar trend was also observed for all other examined organs. Proline
is an endogenous amino acid in mammals that plays an essential role in
primary metabolism and physiologic functions of living organisms. It
can be endogenously synthesized either from glutamate or ornithine.
Proline plays an important role in the synthesis and structure of
proteins and in their metabolism (particularly the synthesis of
arginine, polyamines, and glutamate via P5C), and can act as a direct
substrate for ATP production [[107]29,[108]30]. It has also been
revealed that, under certain conditions, proline present in the brain
can act as a neurotoxin that non-selectively destroys pyramidal and
granule cells in rats [[109]31]. The BALB/c strain had the lowest level
of brain proline but was characterized by the highest levels of other
amino acids such as asparagine, pyroglutamic acid, and N-acetylaspartic
acid. These differences were significant compared to the levels of
these metabolites in brain tissue of CD1 mice.
N-acetylaspartic acid (NAA), one of the most concentrated molecules in
the central nervous system, is synthesized from aspartate and
acetyl-coenzyme A in neurons. Its metabolic and neurochemical functions
are still under investigation, but it is suggested that NAA is a direct
precursor for the synthesis of the important dipeptide neurotransmitter
N-acetylaspartylglutamate. It plays a role in neuronal osmoregulation
and axon–glial signaling as well as in brain nitrogen balance
[[110]32]. Studies on mice and rats have shown that brain
N-acetylaspartic acid concentrations remain relatively constant in
different strains [[111]33]. Considering mice, only two strains (DBA
2J/Sel and C57 BC/cdJ/Sel) had slightly higher levels of
N-acetylaspartic than the other strains tested. We compared quite
different strains, among which BALB/c mice were characterized by the
highest content of this metabolite in the brain compared to C57BL/6 and
CD1.
Valine, along with leucine and isoleucine, belongs to the branched
chain amino acid (BCAA) group which cannot be synthesized de novo in
mammals and must be supplied by a diet. In the central nervous system,
BCAAs play a crucial role by providing nitrogen for the synthesis of
the neurotransmitter glutamate [[112]29]. In the present study, the
level of valine in brain tissue did not differ significantly among the
tested strains of mice. Previous research on (NIH) Swiss mice revealed
that the levels of branched chain aliphatic amino acids (leucine,
isoleucine, allo-isoleucine, and valine) detected in brain tissue were
relatively low compared to other amino acids [[113]34]. The same study
showed that the most prevalent of all free brain amino acids was
glutamic acid, followed by glutamine and aspartic acid. The present
study did not show variability in the levels of the above mentioned
brain amino acids, but we found differences in the levels of
derivatives such as pyroglutamic acid (present in substantial amounts
in the brain and other mammalian tissues [[114]35]) and
N-acetylaspartic acid. The highest levels of these amino acids were
found in the brain tissue of BALB/c mice, whereas the lowest levels
were present in CD1 mice, which suggests strain differences in the
metabolism of glutathione and the neurotransmitter glutamate.
The results of our study suggest that purines (nucleotides, nucleosides
and derivatives) are also among the main metabolites differentiating
strains at the tissue level. They are crucial compounds for cell life.
They are coenzymes, sources of energy, and direct precursors of DNA and
RNA; moreover, they are involved in many other important biological
processes [[115]36]. A comparison of brain tissue in five different
strains, including C57BL/6J and BALB/c, revealed that adenosine levels
measured at basal conditions significantly varied with respect to mouse
strain. Generally, levels of adenosine in the brain have been found to
be low, with the exception of the BALB/c strain, which presented
relatively higher adenosine levels [[116]37]. In our study, the levels
of adenosine monophosphate (AMP) (a direct precursor of adenosine in
cells and a second messenger in many biological processes) detected in
the brain tissue of BALB/c mice were higher compared to C57BL/6 and CD1
mice, which corresponds with the findings cited above. At the same
time, levels of xanthine, an intermediate in the degradation of
adenosine monophosphate to uric acid, were highest in the brain of CD1
mice in comparison to the other two mouse strains, which testifies to
interstrain differences in purine metabolism.
Our research showed that in addition to amino acids and purines,
compounds such as fatty acid esters, ceramides, benzoic acids, and
imidazoles differentiated the liver tissue of the studied strains.
Detected levels of valine, asparagine, pyroglutamic acid, AMP and
2-methylbutyrylcarnitine were highest in the liver tissue of BALB/c
mice among the compared strains, thereby pointing to differences in
relevant metabolic pathways (biosynthesis of panthotenate and CoA,
aminoacyl-tRNA, biosynthesis and degradation of valine, leucine, and
isoleucine, as well as metabolism of purines, glutathione, alanine,
aspartate and glutamate) compared to C57BL/6 and CD1 strains. We did
not see significant differences in the levels of all these metabolites
between C57BL/6 and CD1 mice. Previous reports [[117]26] have revealed
that liver tissue is rich in a wide range of metabolites, including
amino acids, glycerophospholipids, carbohydrates, and steroids likely
indicative of liver-specific pathways. Moreover, it has been proven
that the underlying metabolome of liver tissue in mice is highly
sensitive to genetic differences, which is in agreement with the
results of our study. A previous comparison of different mouse strains,
among which C57BL/6J was present, showed that levels of metabolites
involved in purine and pyrimidine metabolism, as well as pathways that
play a role in amino acid metabolism in the liver, presented
significant differences with respect to strain [[118]28]. Although
different mouse lines were compared in this study, amino acids, purine
nucleotides, nucleosides and purine derivatives were among the main
classes differentiating liver tissue. We also found that liver was the
only tissue in which interstrain differences in allantoin levels were
demonstrated. Allantoin and uric acid are the key compounds of purine
nucleotide catabolism, which are formed in the liver as well as many
other organs in rats [[119]36].
The present study also revealed that strain type exerted a large effect
on the selected metabolites of kidney tissue. We found interstrain
differences in the levels of some amino acids, as well as in the levels
of purines, fatty acids esters, and benzoic acids. The highest levels
of valine and the lowest levels of proline, N-acetylasprtic acid,
N-acetylvaline, AMP, and 2-methylbutyrylcarnitine were found in the
kidney tissue of BALB/c mice, which generally presented a metabolome
that differed considerably compared to C57BL/6 and CD1 mice. Ma et al.
[[120]26] found that most of the detected proteinogenic amino acids
were present at moderate to high levels in kidney tissue relative to
other organs, which can be interpreted as evidence that the metabolite
profile of an organ reflects its biological functions.
Our results for skeletal muscle analyses revealed that strain affected
the levels of some amino acids. The highest levels of asparagine and
pyroglutamic acid and the lowest level of proline were typical of
BALB/c, which according to our PCA analysis, separated best compared to
C57BL/6 and CD1 mice. Interstrain differences were also found in the
levels of pantothenic acid, a water-soluble vitamin; in mice, this
compound plays a vital role in the growth of juveniles and in muscle
maintenance of adults, as it regulates proper muscle mass among other
functions [[121]38]. A pathway analysis revealed that the metabolites
differentiating the strains were involved in the metabolism of amino
acids (arginine and proline) as well as in the biosynthesis of
panthothenate, CoA, and aminoacyl-tRNA. Our results are in agreement
with the previous research, which also demonstrated that strain
affected the metabolomic profiles of skeletal muscle and pathways
involved in energy metabolism (pantothenate and CoA biosynthesis and
TCA cycle) in mice [[122]28]. The same study proved that the examined
tissues (muscle and liver) were largely unaffected by sex, suggesting
that the tissue metabolome remains largely stable across sex.
4. Materials and Methods
4.1. Chemicals
External calibrant Pierce LTQ Velos ESI Positive Ion Calibration
Solution was purchased from Thermo Scientific. All other chemicals were
purchased from Sigma Aldrich (Poznan, Poland). Isopropanol, methanol,
water, acetonitrile and formic acid were LC-MS grade. For SPME fiber
preparation, N,N-dimethylformamide American Chemical Society grade
(ACS) reagent and polyacrylonitrile were used.
4.2. Materials
SPME fibers were manufactured in house, as described by Gomez-Rios et
al. [[123]39]. Probes with a 4 mm extractive phase coating were
manufactured with the use of 5 μm hydrophilic-lipophilic balanced (HLB)
particles provided by Waters (Wilmslow, U.K.).
4.3. Animal Handling and Tissue Collection
BALB/c and C57BL/6 mice were purchased from the Experimental Medicine
Centre of the Medical University in Bialystok, while CD1 mice were
purchased from Jagiellonian University Medical College, Krakow.
Experiments were performed on 15 adult males (aged 12 weeks) for a
total of five mice per strain: BALB/c, C57BL/6 and CD1. The animals
were housed in a controlled environment: temp. 22 ± 2 °C, 12 h
light–dark cycle, humidity 55 ± 10%, with standard mouse chow and water
available ad libitum. The mice were sacrificed by manual cervical
dislocation, which resulted in euthanasia within approximately 10 s.
Once euthanasia was confirmed, brain, liver, kidney and thigh muscle
were immediately collected. According to European Union law, permission
from the Local Ethical Commission is not required for the use of animal
tissue or organs for scientific purposes.
4.4. Solid Phase Procedure and Sample Preparation
SPME fibers coated with a biocompatible sorbent (4 mm length of HLB
extraction phase) were used for the extraction of metabolites from the
selected tissues. Before sampling, all fibers were conditioned
overnight with methanol:water, 1:1, v/v solution. Prior to each
extraction, fibers were rinsed for a few seconds in purified water to
remove residues of organic solvents. A total of two fibers were
inserted per organ for an extraction period of 15 min. Immediately
after sampling, fibers were removed from the organ, quickly rinsed with
water, then gently dried with wipes to remove any residue of the
examined tissue and blood. Afterwards, fibers were placed into empty
0.3 mL polypropylene vials and stored in a freezer at −30 °C until
analysis. Desorption was concurrently performed for all fibers directly
before instrumental analysis. SPME fibers were placed into vials
containing 200 µL of desorption solution consisting of
acetonitrile:water (80:20, v/v) for 120 min with simultaneous vortex
agitation (1500 rpm).
4.5. Liquid Chromatography–High Resolution Mass Spectrometry Analysis
(LC–HRMS)
Samples were analyzed using a LC-HRMS procedure on an ultra-high
performance liquid chromatograph coupled to a Q-Exactive Focus Orbitrap
mass spectrometer. An instrumental method was adopted from Vukovic et
al. [[124]40]. Analytes were separated using a pentafluorophenyl column
(Supelco Discovery HS F5, 2.1 mm × 100 mm, 3 μm). Phase A was water +
0.1% formic acid and phase B was acentonitrile + 0.1% formic acid. The
gradient was as follows: 0–3 min 0% B, 3–25 min linear gradient to 90%
B, 25–34 min 90% B, 34–40 min 0% B. Flow was set to 0.3 mL/min and
injection volume was 10 μL. The column temperature was set to 25 °C and
sample vials were held at 4 °C in the autosampler.
Mass spectrometer parameters in positive ionization mode were as
follows: sheath gas flow rate: 40 a.u.; aux gas flow rate: 15 a.u.;
spare gas flow rate: 0 a.u.; spray voltage 1.5 kV; capillary temp 300
°C; aux gas heater temp 300 °C, S-lens radio frequency (RF) level 55;
S-lens voltage 25 V; skimmer voltage 15 V. Scan range was set on m/z
80–1000 with resolution 70,000. Acquisition was performed using
automatic gain control (AGC) target 1E6 and inject time to C-trap was
set on auto. The instrument was calibrated using external calibration
every 72 h, resulting in mass accuracy <2 ppm. Within-sequence samples
were randomized. Pooled quality control (QC) samples composed of 10 µL
of each sample were run every 12 injections to monitor instrument
performance.
The structure of selected compounds was confirmed based on suitable LC
retention time and <3 ppm mass accuracy. Full MS/dd-MS2 confirmation
mode was used for this purpose. Fragmentation parameters were as
follows. Mass resolution: 35,000 full width at half maximum (FWHM), AGC
target: 2E4, minimum AGC: 8E3, intensity threshold: auto, maximum IT:
auto, isolation window: 3.0 m/z, stepped collision energy: 10 V, 20 V,
40 V, loop count: 2, dynamic exclusion: auto.
4.6. Data Processing and Statistical Analysis
Raw MS data was processed by Compound Discoverer 2.1 (Thermo Fisher
Scientific) to putatively identify metabolites. QC-based area
correction was set to min 50% QC coverage and max QC area relative
standard deviation (RSD) 30%. Only features with min peak intensity 105
and signal to noise ratio > 3 were taken into consideration. A data
filtration step removed 8795 (93%) of the 9468 features. Putative
identification of features was performed by searching for the exact
molecular weights of identified features (3 ppm accuracy) in the Human
Metabolome Database (HMDB) and Kyoto Encyclopedia of Genes and Genomes
(KEGG) online databases. Annotations were verified according to the
isotopic distribution of molecules using the ChemSpider Database as a
reference ([125]Table S4). The MetaboAnalyst 4.0 software and the KEGG
pathway library corresponding to the Mus musculus metabolome were used
to obtain metabolic pathways associated with significantly differential
metabolites. Identification of spectra of fragmented compounds was done
with the use of Thermo Scientific FreeStyle 1.4 software linked to
online mzCloud database ([126]Table S4).
The averaged peak areas (from two independent SPME fiber replicates)
for the obtained compounds were analyzed using Statistica 13.3 PL
software (StatSoft, Inc., Tulsa, Oklahoma, USA) via one-way analysis of
variance (ANOVA). A post-hoc Tukey Honestly Significant Difference
(HSD) test was used to determine the significance of differences among
groups, where a p-value of < 0.05 was considered to be significant.
Resulting data were then exported to the (PLS)-Toolbox (Eigenvector
Research Inc.) in Matlab^® version 2018b (MathWorks Inc., Natick, MA,
USA) for multivariate statistical analysis. Data was log-transformed
and mean-centered prior to principal component analysis (PCA) and
partial least squares discriminant analysis (PLS-DA). The PLS-DA model
was cross-validated using the venetian blinds method and refined by
random permutation (100 times) of the Y variable. Two-dimensional score
plots were generated to visually assess separation between sample
groups.
5. Conclusions
The results of the present study clearly indicate that the baseline
metabolomic profiles of organs, especially brain and liver tissue, as
well as different metabolic pathways, vary widely among the laboratory
mouse strains commonly used in biomedical research. Interstrain
differences in the metabolome at the tissue level testify that even
general-purpose models can give different answers to the same
experimental factors, and as a result, yield contradictory outcomes in
a study if such factors are not accounted for. For this reason, close
attention should be paid when choosing a mouse strain for a particular
purpose of scientific research, and considerations of strain
variability should also be included in the interpretation of results.
Further, the present study corroborates that SPME is an easy, quick and
reliable sample preparation method that is ideally suited for tissue
analysis in metabolomic studies. However, one needs to remember that
the simplicity of the method compromises coverage of analytes compared
to multi-step and time-consuming liquid–liquid extraction, and for
in-depth investigation of all metabolites, a traditional approach
should be considered. Also, it must be pointed out that our results are
based mainly on the putative identification of metabolites, so further
LC–MS/MS analysis with a high level of confidence is still required to
confirm the identities of all metabolites.
Acknowledgments