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Insights into amino acid
fractionation and incorporation
by compound‑specic carbon
isotope analysis of three‑spined
sticklebacks
Tobias Hesse1, Milen Nachev2,3, Shaista Khaliq1, Maik A. Jochmann1,3*, Frederik Franke4,6,
Jörn P. Scharsack4,5, Joachim Kurtz4, Bernd Sures2,3 & Torsten C. Schmidt1,3
Interpretation of stable isotope data is of upmost importance in ecology to build sound models for
the study of animal diets, migration patterns and physiology. However, our understanding of stable
isotope fractionation and incorporation into consumer tissues is still limited. We therefore measured
the δ13C values of individual amino acids over time from muscle and liver tissue of three‑spined
sticklebacks (Gasterosteus aculeatus) on a high protein diet. The δ13C values of amino acids in the
liver quickly responded to small shifts of under ± 2.0‰ in dietary stable isotope compositions on
30‑day intervals. We found on average no trophic fractionation in pooled essential (muscle, liver) and
non‑essential (muscle) amino acids. Negative Δδ13C values of − 0.7 ± 1.3‰ were observed for pooled
non‑essential (liver) amino acids and might indicate biosynthesis from small amounts of dietary lipids.
Trophic fractionation of individual amino acids is reported and discussed, including unusual Δδ13C
values of over + 4.9 ± 1.4‰ for histidine. Arginine and lysine showed the lowest trophic fractionation
on individual sampling days and might be useful proxies for dietary sources on short time scales.
We suggest further investigations using isotopically enriched materials to facilitate the correct
interpretation of ecological eld data.
Stable isotope analysis (SIA) of carbon is a powerful tool in ecological studies to investigate resource utilization,
foraging behavior and migration patterns of animals1–3. e underlying principle is that the carbon stable isotope
composition (δ13C) of diets is mostly retained by consumers, with only small amounts of trophic fractionation
occurring during incorporation and metabolism of nutrients. In bulk stable isotope analysis (BSIA), trophic
fractionation therefore only increases the δ13C values of consumers by 0–1‰4, 5 and the carbon stable isotope
signature of an individual is mostly depending on the isotope composition of primary producers. is enables
tracking of nutrients from the base of food webs to higher trophic level predators. However, there can be devia-
tions from this pattern depending on species, analyzed tissue and diet composition6, 7. A more recent approach,
compound specic stable isotope analysis (CSIA), was enabled by analyzing individual compounds rather than
bulk tissues. Where BSIA of carbon typically shows little fractionation between diet and consumer, individual
constituents such as amino acids (AA) can have higher fractionation, depending on which compounds they are
routed from and the nutrient composition of diets3, 8–12.
AAs can be divided into essential amino acids (EAA) and non-essential amino acids (NEAA, Table1). EAAs
cannot be synthesized by higher organisms and therefore need to be taken up directly from dietary sources, lead-
ing to no or very little trophic fractionation as they are traversing the food chain mostly unchanged from primary
producers to top predators3, 13–15. NEAAs on the other hand can be synthesized de novo in higher organisms and
OPEN
1Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany. 2Aquatic
Ecology, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany. 3Centre for Water and Environmental
Research, University of Duisburg-Essen, Universitätsstr. 5, 45141 Essen, Germany. 4Institute for Evolution &
Biodiversity, University of Münster, Hüerstr. 1, 48149 Münster, Germany. 5Present address: Thünen Institute of
Fisheries Ecology, Herwigstr. 31, 27572 Bremerhaven, Germany. 6Present address: Bavarian State Institute of
Forestry, Hans-Carl-von-Carlowitz-Platz 1, 85354 Freising, Germany. *email: maik.jochmann@uni-due.de
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therefore originate either from dietary protein and/or other macronutrients, such as lipids and carbohydrates.
Whether these compounds are directly routed or synthesized de novo is depending on dietary nutrient composi-
tion, e.g. consumers fed with high protein contents tend to also directly incorporate NEAAs in order to preserve
energy11, 13, 16, 17, whereas biosynthesis of NEAAs from lipids leads to lower δ13C values because lipids are depleted
in 13C compared to proteins or carbohhydrates18, 19. However, the classication of AAs into essential and non-
essential is not always clear, as nutrient requirements of some NEAAs, e.g. glutamic acid (Glx) and proline (Pro),
can outmatch an organism’s ability to synthesize these compounds, rendering them temporarily or conditionally
essential20. Another special case would be tyrosine (Tyr), which can be synthesized de novo in higher organisms
but is directly derived from phenylalanine (Phe), which is an EAA. e carbon isotope signature of Tyr therefore
typically falls into the same range as Phe12, although it should be considered non-essential.
One shortcoming of most ecological studies using stable isotopes is that they typically only consider whole
body or muscle tissue21, as these are easy to access. e turnover and incorporation rate in muscle tissue is rather
low and more aected by consumer physiology and growth phases during which protein synthesis and deposi-
tion occurs22, 23. e stable isotope signature of muscle tissue therefore reects the long-term dietary intake
and remains conservative towards small or only seasonal changes in dietary δ13C values. e liver, on the other
hand, responds more quickly to small or seasonal dietary changes, as its regulatory activities require continuous
protein turnover22–26. In addition, the liver plays an important role in the metabolism and biosynthesis of AAs20,
27, 28, which makes it an ideal tissue for studying these processes. Another shortcoming of most studies using
stable isotopes is that they are eld based, hence there was an initial call for more laboratory studies in 199729 to
understand the fundamental principles of isotopic incorporation, trophic discrimination and isotope routing.
Although the number of laboratory-based studies has increased since then, the call was renewed in 200930 to
facilitate correct interpretation of eld data. Few controlled feeding experiments so far examined the carbon
isotope fractionation of individual AAs between diet and sh consumers, with varying magnitudes and direc-
tions of trophic fractionation reported10–13, 17. McMahon etal. (2010) found 13C-depleted isotope signatures of
Gly between Common Mummichogs (Fundulus heteroclitus) and one of their diets, whereas Rogers etal. (2019)
found signicantly 13C-enriched Gly stable isotope signatures of Chinook Salmon (Oncorhynchus tshawytscha)
reared on the same diet. is example demonstrates that clear dierences in individual AA trophic fractionations
can occur among sh species.
e three-spined stickleback (Gasterosteus aculeatus) is a well-studied model sh in ecology, evolutionary
biology and parasitology31, 32, yet no carbon stable isotope analysis of AAs has been done to this date. e only
studies reported so far are eld-based and use BSIA to investigate sex, armor, phenotypes, genetics and host-
parasite relationships33–36. We conducted a laboratory-based feeding experiment, where three-spined sticklebacks
were reared on a protein rich diet with low amounts of lipids (60% protein, 5% lipids) over the course of four
months. To contribute to our understanding of isotope incorporation, discrimination and routing, we measured
the carbon stableisotope signature of individual AAs from muscle, liver and dietary samples taken on 30-day
intervals by Liquid Chromatography Isotope Ratio Mass Spectrometry (LC-IRMS). Previous BSIA of dietary
samples (data not published) revealed a minor carbon stableisotope shi from − 15.8‰ aer 30days to − 17.0‰
aer 60days, − 18.1‰ aer 90days and − 16.9‰ aer 120days, which is likely transferred to sh tissue to varying
degrees depending on tissue type. Based on our current knowledge of isotope incorporation, fractionation and
the high protein contents in sh diets, we expect that both EAAs and NEAAs in muscle and liver tissue of stick-
lebacks will be mainly routed from dietary sources and therefore show little trophic fractionation. Furthermore,
if the dietary stable isotope shi over time from BSIA is also measurable using CSIA of individual AAs, we expect
that the high protein turnover of liver tissue will also result in a signicant isotope shi between sampling days
in contrast to muscle tissue. Lastly, the small amounts of other macronutrients than dietary protein might not be
enough to result in signicantly dierent δ13C values between liver and dietary samples, but using multivariate
analysis might indicate patterns of fractionation due to biosynthesis or metabolism of NEAAs. is is the rst
time that carbon isotope signatures of AAs were measured for three-spined sticklebacks on a time series and in
a controlled feeding experiment. Our goals were to investigate δ13C values of AAs from liver and muscle tissue,
especially in response to changing dietary δ13C values over time and to nd dierences in δ13C patterns between
tissues. Furthermore, we expect that NEAAs and EAAs will show similar and small trophic fractionation in
Δδ13C values in general, since sh are fed on high protein contents. Our results will therefore help to validate
Table 1. Classication of analyzed AAs in sh into essential/non-essential and glucogenic/ketogenic.
Abbreviations are given in brackets. Adapted from Falco etal. (2020)72.
Glucogenic Glucogenic/ketogenic Ketogenic
Essential
Arginine (Arg) Phenylalanine (Phe) Lysine (Lys)
Histidine (His)
reonine (r)
Nonessential
Alanine (Ala) Tyrosine (Tyr)
Asparagine/aspartate (Asx)
Glutamine/glutamate (Glx)
Glycine (Gly)
Proline (Pro)
Serine (Ser)
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common assumptions in CSIA-AA, but also show limitations and new possibilities in ecology to interpret the
carbon stable isotope signatures of AAs from dierent tissue types and time intervals.
Results
AA δ13C changes between sampling days. One-way analysis of variance (ANOVA) for each AA with
sampling days as independent variable and δ13C values as dependent variable of dietary samples revealed a sig-
nicant isotope shi over time for all AAs except His (DF = 3, 8; p < 0.01; TableS2). A trend can be seen where the
carbon stable isotope signature of each dietary AA decreased between 30 and 60days as well as 60 and 90days,
followed by an increase in δ13C values between 90 and 120days (Fig.S2), although the shi was not always sig-
nicant between consecutive sampling days (30–60, 60–90, 90–120days). e highest dierences between − 2.4
and − 4.2‰ were observed between 30 and 90days. Isotope signatures of AAs in the liver were signicantly dif-
ferent over time for Ala (F3,8 = 6.8, p = 0.004), Asx (F3,8 = 9.2, p = 0.001), Arg (F3,8 = 13.7, p < 0.001), Lys (F3,8 = 18.4,
p < 0.001), Phe (F3,8 = 8.9, p = 0.001) and Tyr (F3,8 = 20.8, p < 0.001). Most of these dierences were also observed
between 30 and 90days, with the addition of Glx between those specic sampling days and decreasing δ13C
values between − 2.1 and − 3.0‰. Although not all AAs in the liver revealed a signicant isotope shi, the trend
of decreasing δ13C values from 30 to 60days and from 60 to 90days followed by an increase from 90 to 120days
was still comparable to dietary samples. No signicant dierences between sampling days were observed for
muscle tissue for any AA.
Trophic fractionation of NEAAs, EAAs and individual AAs among liver and muscle. We inves-
tigated trophic fractionation between NEAAs and EAAs over the complete sampling period. Two-way ANOVA
on Δδ13C values of pooled NEAAs and EAAs (NEAA/EAAs and tissue as xed factors, TableS4) revealed
no signicant dierence in trophic fractionation between NEAAs and EAAs (F1, 435 = 5.6, p = 0.019, α = 0.01)
among all samples, but there was a signicant interaction between factors (F1, 435 = 9.4, p = 0.002). e interac-
tion was caused by negative Δδ13C values of − 0.7 ± 1.3‰ for NEAAs in liver tissue compared to Δδ13C values
of − 0.1 ± 1.1‰, 0.0 ± 1.4‰ and 0.0 ± 1.1‰ for EAAs in liver and both NEAAs and EAAs in muscle, respectively,
resulting in signicant dierences in trophic fractionation around ± 0.8‰ between those samples (Tukey’stests,
TableS4). Dierences in trophic fractionation between EAAs in the liver and NEAAs and EAAs in muscle were
not observed.
Δδ13C values of individual AAs between sh tissues and diets were calculated over the complete sampling
period and on each sampling day (Fig.1, TableS3). Signicant dierences were tested with two-sided t-tests for
each value (N = 20 for the complete sampling period, N = 5 on each sampling day) and with a condence level of
0.01. Signicant negative Δδ13C values over the complete sampling period were measured for Asx (− 0.8 ± 1.0‰),
Glx (− 0.8 ± 1.1‰), Ser (- 1.8 ± 1.2‰), Tyr (− 1.2 ± 0.8‰) and Phe (− 1.1 ± 0.9‰) in liver tissue and for Tyr
(− 1.5 ± 1.0‰) and Phe (− 0.7 ± 0.9‰) in muscle tissue, whereas positive Δδ13C values were measured for Ala in
muscle (1.0 ± 1.3‰), r in liver (0.9 ± 1.0‰) and His in both tissues (8.2 ± 1.2‰ and 4.9 ± 1.4‰, respectively).
In some of the mentioned cases, average Δδ13C values are lower than the measured SD, but due to the larger
sampling size (N = 20 over the complete sampling period) the resulting p-values were still below the signicance
level of 0.01.
e highest Δδ13C values on individual sampling days in liver tissue were measured for His (6.8 ± 0.5‰ on
day 30, 8.5 ± 0.9‰ on day 60, 8.7 ± 1.1‰ on day 90 and 8.7 ± 0.8‰ on day 120), for Gly on day 90 (1.8 ± 0.9‰)
and day 120 (1.7 ± 0.7‰) and for both Tyr (− 0.7 ± 0.4‰ on day 30, − 1.0 ± 0.4‰ on day 60 and − 1.7 ± 0.5‰ on
day 120) and Phe (− 1.7 ± 0.6‰ on both day 30 and 120). Signicant Δδ13C values were measured in muscle tis-
sue on day 90 for Ala (2.5 ± 0.3‰), Asx (1.7 ± 0.5‰), Glx (1.8 ± 0.7‰), Ser (2.0 ± 0.6‰), His (6.6 ± 0.9‰), Lys
(1.3 ± 0.4‰) and r (2.0 ± 0.7‰). Pro, Arg and Lys had the smallest average Δδ13C values in both muscle and
liver tissue around ± 0.1 to 0.2‰ (Arg, Lys) and around − 0.5‰ (Pro). Except for Lys in muscle on day 90, no
signicant fractionation on individual sampling days were measured for those three AAs. e most signicant
isotope fractionation was observed for His between diet and both liver and muscle. Since peak areas of His were
low compared to the earlier and closely eluding Lys (Fig.S1), we tested dierent background algorithms (indi-
vidual, dynamic and manual) in the Isodat 2.0 soware to check if the dierences between His δ13C values could
be explained by interferences and coelution of the more abundant Lys. Regardless of the used peak integration
and background detection procedure, a strong isotope fractionation of His between dietary and liver/muscle
was always observed.
Patterns of AA δ13C values among tissues. Since dierences in δ13C values between sh and dietary
samples might be low in response to the used high protein diets, we performed ANOVA simultaneous com-
ponent analysis (ASCA) to investigate patterns of AA δ13C values in a multivariate approach. Sample scores of
liver tissue were separated from muscle and dietary samples on the rst principal component of factor 1 (Tissue,
Fig.2), which contributed 22.4% to the total variance. NEAAs were the main driver of separation with loadings
above 0.3 for Ala, Asx, Glx and Ser, whereas Gly was the only NEAA with a negative loading of below −0.2
(Fig.2). Ser had the overall highest loading of 0.7 on PC1. All EAAs and Pro had loadings of below ± 0.2 and
therefore little to no eect on PC1 separation. Individual scorings of liver samples were negative on PC1, whereas
muscle and dietary samples had almost exclusively positive scorings. PC2 showed slight separation between sh
tissue and diets and was mostly loaded with the EAAs Phe, r as well as the NEAAs Pro and Tyr (Fig.2). Arg
and Lys were the only AAs which had no impact on separation between tissues for both PCs. Factor 2 (sampling
days) of the ASCA had an eect of 35.5% on system variance and PC1 accounted for 93.5% of variance on factor
2. AA loadings on PC1 were exclusively positive between 0.15 and 0.4. Sample scores centered around + 4 for
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samples taken aer 30days, 0 for samples aer 60days, − 3 for samples aer 90days and − 1 for samples aer
120days, but clear separation of samples was not achieved in a score plot.
Discussion
e cause for the stable isotope shi of dietary AAs over time in our study remains unknown, but the observed
trend of all AAs having a similar shi over time indicates that dierent batch materials of mosquito larvae might
have been used throughout the experiment. e change of δ13C values in muscle tissue between any given sam-
pling day was within −1.8 and + 1.2‰, but these shis were statistically not signicant and overshadowed by
natural variations between individuals. is supports the idea that AAs from muscle tissue are a more conserva-
tive indicator of long-term dietary intake in sh, similar to what was previously described for BSIA22, 23. Liver
tissue, on the other hand, showed signicant isotopic dierences over time, indicating that the high nutrient
turnover rate of the liver is reected in its AA carbon stable isotope signature and even short-term changes are
visible. e range of δ13C values in the liver was between −3.3 and + 1.4‰ and EAAs showed the most signi-
cant dierences (one-way ANOVA, p ≤ 0.001), which indicates high turnover for these compounds because they
must be rooted from dietary sources. Only Ala and Asx showed signicant dierences over time from NEAAs
in liver tissue. Both AAs are important energy substrates in sh and precursors for gluconeogenesis37, which
is the metabolic pathway in the liver to synthesize glucose from other carbon substrates. Fish therefore have a
high Ala and Asx demand in liver tissue, which might outmatch the sh’s ability to suciently synthesize these
compounds and lead to an increased incorporation or turnover from dietary sources. According to our results,
liver carbon isotope signatures of Ala, Asx, Arg and Lys might be useful proxies to track consumer diets of small
teleost sh on protein rich diets and on time scales of at least 30-day intervals. is information could possibly
be expanded to non-protein rich diets for Arg and Lys, due to the necessity of eukaryotes to directly incorporate
these nutrients from dietary source, regardless of their composition13. e change in liver δ13C values were most
notable between samples from day 30 and 90 and not consistently between consecutive sampling days, which
can be attributed to the rather small changes in dietary δ13C values of only ± 1 to 2‰ between sampling days,
which are also partly overshadowed by natural variations and measurement uncertainties. e isotopic half-life of
muscle and organ tissue from ectotherms can be estimated according to their body mass38. e average body mass
of sticklebacks on sampling days was 1.28 ± 0.35g (N = 20), which would translate to a half-life of approximately
Figure1. Carbon stable isotope signatures of AAs show low trophic fractionation between sh liver/muscle and
dietary samples except for His. Trophic fractionation between stickleback and dietary samples was estimated by
calculating Δδ13C values ± SD (error bars, n = 5) for liver ( ) and muscle ( ) samples. Asterisks over Δδ13C
values indicate signicant dierences from two-sided t-tests against 0 (p < 0.01, TableS3). Δδ13C values are
generally below ± 2‰ for all AAs in muscle and liver samples except for His. e frequently signicant Δδ13C
values in muscle samples on day 90 are caused by the low protein turnover and therefore minor decrease of δ13C
values in muscle samples compared to the signicant δ13C decrease in dietary samples. Arg and Lys have the
lowest trophic fractionation overall.
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27days for muscle and 12days for liver tissue. is estimation supports the observed signicant change of
δ13C values over time in liver samples and the only slightly decreasing δ13C in muscle tissue. e dierences in
dietary AA δ13C values were not consistent between sampling days, which further limits our ability to compare
the dierent trends observed in muscle and liver tissue. However, the most consistent isotope shi was observed
during the rst 90days of the experiment with two consecutive negative changes in dietary AA δ13C values. ese
changes are also reected during that period in liver tissue, which highlights the potential of CSIA to dierentiate
between even small dierences of dietary intake. Although we did not quantify the exact cellular turnover rate
of individual AAs in stickleback tissues, knowing an estimated timeline for evaluating the diet of an animal is
useful for ecological studies investigating migration or seasonal changes of food sources. It has been shown that
choosing dierent tissue types in individuals enables researchers to explore temporal and spatial resource use of
animals and it is critical to consider incorporation rates of isotope signatures into these tissues39, 40. Most studies
so far on isotopic incorporation and turnover rates are still based on BSIA41–43 and enhancing our knowledge to
specic compounds in dierent tissues can provide powerful opportunities to study the physiology of migrating
animals, which are hard to capture multiple times. In addition, the analysis of AA isotope signatures of these
animals from tissues with dierent incorporation rates can help to illuminate temporal variations and identify
endogenous vs. exogenous resources, without the need of multiple sampling points.
e initial amino acid isotope signature of mosquito larvae used for feeding shows typical patterns also seen
in other studies, where Gly and Ser are the most 13C-enriched AAs and EAAs are isotopically 13C-depleted in
comparison to NEAAs3, 8–13, 15, 17, 44, 45. NEAAs can be synthesized de novo in higher organisms, leading to isotopic
fractionation during metabolism and nutrient ow from primary producers to top predators in a given food
web17. It is known that the carbon stable isotope composition of NEAA in higher organisms is therefore exible
and varies according to dietary protein and lipid intake. A study from McMahon etal. (2010)13 fed common
mummichogs on four isotopically distinct diets with dierent protein and lipid compositions and measured the
carbon stable isotope signature of individual AAs from muscle tissue. In two high protein diets based on clam
and squid, they found Δδ13C values between −4 and − 7‰ for Ala, Ser and Gly, whereas trophic fractionation
was lower for other NEAAs and absent for EAAs. e protein and lipid contents in both diets were 70% and 18%,
respectively. Another study from Newsome etal. (2014)46 fed rodents diets of dierent protein and lipid contents
with distinct isotope signatures and found that NEAA δ13C values in muscle tissue shied more towards dietary
lipids with lower protein/lipid ratios, whereas a protein/lipid ratio of 40%/5% resulted in NEAA δ13C close to
the protein source. Our study used commercially available dried mosquito larvae with a protein/lipid content of
60% and 5%, respectively, and therefore represents a diet with a higher protein to lipid ratio than previous
Figure2. Multivariate analysis of δ13C values shows distinct δ13C patterns of NEAAs in liver samples compared
to dietary and muscle samples. Biplot of sample scores from ASCA for factor 1 (tissue) separates between liver
() and muscle ( ) or dietary ( ) samples on PC1. Scores of liver samples on PC1 are negative in contrast
to positive scores of muscle samples or scores around 0 for dietary samples. NEAA loadings ( ) on PC1 are
positive for Ala, Ser, Asx and Glx and negative for Gly, whereas EAAs ( ) and Pro have no impact on PC1. AA
loadings on PC2 are positive for Pro, Phe and Tyr and negative for r.
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studies. Our results show low isotope fractionations for NEAAs and EAAs between diets and sh tissue and
support the idea of isotope routing when animals are fed protein rich diets, which is energetically favorable
compared to de novo synthesis. High isotope fractionations of ± 5‰ of some NEAAs between diet and consumer,
as described by other studies11, 13, did not occur in our case, probably due to the low abundance of other macro-
nutrients. Fractionation between muscle and dietary samples was visible for Ala, Asx, Glx, Ser, His, Lys and r
especially on day 90, but these dierences are a direct result of muscle tissue not responding to the shiing stable
isotope signatures in diets and not due to trophic fractionation. e highest dierence in dietary δ13C values
over time were observed between day 30 and 90 of the feeding experiment. Since muscle tissue has a low turnover
rate and is a more conservative indicator of long-term dietary intake, the lack of response to the short-term
isotopic shis in dietary samples results in higher dierences between muscle and dietary samples on day 90.
Small but signicant Δδ13C values were observed between liver and dietary samples for Asx, Glx, Ser and Tyr
over the whole sampling period and occasionally for Ala and Gly on individual sampling days. e trophic
fractionation of Ser in the liver was striking and consistently between −1 and −2‰ and Gly was the only NEAA
who showed an opposite trend of positive Δδ13C values on day 90 and 120. Catabolic pathways for Ser involve
deamination to pyruvate, transamination with pyruvate to form hydroxy pyruvate and Ala and formation of Gly
with tetrahydrofolate47. Additionally, participation of Gly into gluconeogenesis requires the conversion of Gly
to Ser by serine hydroxy methyltransferase and both AAs participate further in sulfur and one-carbon
metabolism20. e liver plays a major role in energy and AA metabolism by regulating and controlling gluconeo-
genesis as well as synthesizing many of the NEAAs28. Synthesis of NEAA carbon backbones can occur from either
glycolytic intermediates for Gly, Ser and Ala or from Krebs cycle intermediates for Asx and Glx48. It is, however,
hard to estimate the contribution of isotopic fractionation for each of these pathways, not only because intrinsic
fractionation and mass ow are unknown, but also because metabolic pathways are oen intertwined and hard
to separate in living organisms at natural abundance level. Enzymatic reactions and catabolism of nutrients are
usually associated with discrimination against the heavier isotopes, in this case 13C, leading to depleted signatures
in the product, while the educt of the reaction becomes isotopically 13C-enriched49. e small enrichment of Gly
observed in liver could therefore be indicative of extensive conversion of Gly to other compounds, with Ser being
one possibility and resulting in more noticeable negative Δδ13C values. It is, however, a far stretch at this point
to account the small isotopic dierences in Ser and Gly to specic metabolic or enzymatic reactions, especially
since metabolism of Gly and Ser involve several dierent pathways. Future research and controlled feeding
experiments could focus on using isotopically labeled substitutes to track individual metabolic pathways, such
as the conversion of Gly and Ser. Pro was the only NEAA in liver tissue showing no trophic fractionation at all
over the sampling period, which points more to a behavior like an EAA being routed by diets. As mentioned in
the introduction, Pro can be synthesized in higher organisms, but the classication as a NEAA is sometimes
misleading as metabolic requirements might heavily outmatch de novo synthesis. Our results indicate that sh
fed on high protein contents incorporate Pro mostly from dietary sources leading to no or very little fractiona-
tion in both liver and muscle tissue. Arg and Lys were EAAs with very low fractionation overall, strongly follow-
ing the trend of dietary isotope signatures even in muscle tissue over short time periods. Arg is highly abundant
in sh protein and tissue uid20, serving as a precursor for the synthesis of proteins, nitric oxide, urea, Pro, Glx
and creatine50. Arg is of special interest here because it is not oen reported in the literature, since most studies
apply GC-IRMS for measurement of carbon stableisotope signatures of AAs, which requires derivatization and
results in the loss of Arg51, 52. Because of the high abundance, demand and turnover of dietary Arg in sh leading
to very little fractionation even in muscle tissue over a short-term dietary isotope shi, it could serve as another
proxy in addition to Phe, Leu, Ile and Lys10–13 to track carbon sources from primary producers in muscle or liver
tissue. Identifying EAAs with very low fractionation patterns is pivotal for ecological studies using isotope n-
gerprinting to better constrain the source of end-member signatures and study the carbon ow in terrestrial and
oceanic environments3, 8, 9, 13–15, 17. Phe and His were the only EAAs showing consistently dierent isotope sig-
natures between diet and sh tissue, which is surprising since they need to be directly taken up and rooted from
dietary sources. Phe can be converted to Tyr and the depleted isotopic signature of Tyr compared to diets could
therefore also be explained by enzymatic fractionation discriminating against the heavy carbon isotopes.
Although EAAs cannot be synthesized by eukaryotes, the fundamental assumption that these constituents must
be solely sourced from dietary protein resulting in low isotope fractionation of ~ 0‰ has not always been met.
ere are studies suggesting that gut microbes can contribute to the homeostasis of EAAs in animals by de novo
synthesis, which of course complicates their use as stable and robust proxies for isotope ngerprinting17, 53–56.
e contribution of microorganisms in the gut of sticklebacks to the EAAs homeostasis could explain the
observeddierence of Phe and Tyr isotope signatures in sh muscle and liver tissue compared to diets and high-
lights the importance to experimentally explore the variation of EAA isotope fractionation by thegut microbi-
ome. According to our results, Arg and Lys might therefore be better suited in sticklebacks and possibly other
teleost species to estimate their carbon ow and allocate resource consumption, if the contribution of gut sym-
bionts is of no interest. His showed overall the highest fractionation among the analyzed AA. We investigated
possible inuences of the closely eluding and much more abundant Lys on the δ13C values of the less abundant
His by varying peak integration and background detection methods in the ISODAT 2.0 soware, but found no
indication that the high fractionation patterns were caused by chromatographic interferences. Dierences in the
peak area ratios of Lys/His were dierent between tissues, but the observed trend of increasing ratios of Lys/His
from dietary to liver and nally muscle tissue does not match the increasing δ13C patterns from dietary to muscle
and then liver tissue. Although we cannot exclude any inuence of poor peak resolution between Lys and His
and the dierent ratios on their carbon isotope signatures, we think that these inuences would not result in the
high fractionation patterns that we observed. is is also supported by an earlier study showing that poor peak
resolution in LC-IRMS does not greatly inuence the measured carbon isotope signatures, as compared to GC-
IRMS57. e previously mentioned de novo synthesis and contribution of EAAs from gut microbes could of
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course be one explanation for the highly 13C-enriched isotope signatures of His in in stickleback liver and muscle
tissue. Other reasons could include the enzyme histidine decarboxylase, which is produced by bacteria and causes
histamine sh poisoning58, 59. It has been speculated before that histidine decarboxylase might still be released
from autolyzing of bacterial cells during and aer freeze drying and convert histidine to histamine60. Enzymatic
reactions are known to cause isotopic fractionation, which usually discriminates against the heavier isotope49
and could result in enriched isotope signature of the remaining histidine in sh tissue. Interestingly, an early
study investigating histamine concentrations during storage of esh and liver tissue of mackerel under dierent
conditions showed a higher increase of histamine concentrations in the liver61, which would t to the higher
isotope values of histidine in stickleback liver compared to muscle tissue, if it was caused by enzymatic reactions.
Testing this, however, would require histamine isotope measurements in dietary and sh samples to compare
isotope signatures and was out of scope of this study. Another explanation would be that sticklebacks fed on
mosquito larvae lack dietary histidine to match their metabolic requirements. We did not directly measure the
protein and AA content of dietary mosquito larvae, but His has been mentioned in the literature as a more fre-
quent limiting AA when animals are fed with insect meals62. His is an important amino acid for growth, tissue
formation and hemoglobin synthesis in sh63 and a lack of dietary His may lead to increased fractionation in
consumer tissue due to catabolism during starvation. ere is however no consensus so far on the isotopic eect
of starvation or nutritional stress on consumer tissue64 especially for single compounds, and such eects could
be subject for further studies.
ASCA provided an alternative way to PCA and LDA as classical multivariate analysis and was especially
useful in our case because we could incorporate the structure of our experiment into the model, with sampling
days and tissue types as two separate factors65. is removed a lot of “noise” from the dietary isotope shi when
investigating AA δ13C patterns between tissue types. All NEAAs except Pro had high positive (Ala, Asx, Glx and
Ser) or negative (Gly) loadings on PC1, whereas loadings of EAAs and Pro were low. Sample scores on PC1 were
negative for liver samples and either 0 or positive for muscle and dietary samples, which shows that NEAAs in
the liver have distinct δ13C patterns. Since liver samples had exclusively negative scorings on PC1, NEAAs with
positive loadings (Ala, Asx, Glx and Ser) therefore showed a pattern of 13C-depletion compared to muscle and
diets. As mentioned earlier, the liver plays a major role in biosynthesis of NEAAs, which is dependent on dietary
content of macronutrients. Because we used a diet of high protein and relatively low lipid composition, synthesis
of NEAA from other nutrients in the liver might be limited but is still visible when combining δ13C values of
individual AAs in a multivariate approach. Lipids in natural samples typically have lower δ13C values compared
to AAs or carbohydrates18, 19 and partial biosynthesis of NEAAs from lipids in the liver would therefore result in
slightly lower δ13C values. e observed low loadings of EAAs on PC1 support this assumption since they cannot
be synthesized denovo from other macronutrients in the liver and therefore do not show distinct δ13C patterns
between muscle and liver tissue. Sample scores of factor 2 (sampling days) from ASCA reected the changing
δ13C values in samples induced by the dietary isotope shi, with decreasing δ13C values during the rst 90days
and a small increase aer 120days. Since all AA loadings on factor 2 were positive and within 0.15 to 0.4, the
shi of δ13C values over time was similar between individual AAs.
Conclusion
Studying the isotope signature of AAs between diet and consumer gained much attraction during the last years,
yet our knowledge of the fundamental principles behind isotope incorporation and fractionation of individual
substitutes is still limited. Our study shows that direct isotope routing even of NEAAs might still be the pre-
ferred way for nutrient assimilation when sh are fed with high protein diets, and there are not only dierences
in isotopic turnover rates between muscle and liver, but also dierent isotopic behaviors that individual AAs in
these tissues show based on their metabolic role. However, investigating isotope fractionation of AAs on natural
abundance levels might not be the best approach to study fundamental incorporation and turnover patterns,
since a lot of information might be lost to natural variations and measurement uncertainty. Although the use of
multivariate analysis can help to nd general patterns of δ13C values between tissue types, we recommend the
use of 13C-enriched materials in controlled feeding experiments to accurately track metabolic pathways. One
good example to investigate could be the mentioned conversion of Gly and Ser or biosynthesis of NEAAs in the
liver, which has the potential to greatly improve our understanding of nutrient assimilation and conversion and
is long overdue for the correct analysis and interpretation of eld data. On the other hand, it is promising to see
that even small dierences and uctuations of isotope values, which are more realistically encountered in nature,
can be investigated using CSIA of dierent sample materials.
Materials and methods
Feeding experiment. ree-spined sticklebacks were laboratory-raised ospring and reared in twelve 14 L
tanks (VewaTech, Germany) as part of a parasitic infection experiment. Water was recirculated and held at 18°C
with a 15h light and 9h dark cycle. Stickleback ospring were produced by invitro fertilization from individu-
als collected from a brook in North-West Germany (52° 17′ 33.11″ N, 7°36′ 46.48″ E), about eight months old
at the beginning of the experiment and fed daily with washed red mosquito larvae (Chironomidae) over the
course of four months. Each lab tank contained twelve individuals, which were divided into two groups. e rst
group consisted of three individuals per tank, which were exposed to an uninfected copepod as a sham-exposed
control group, while the other nine individuals per tank were exposed to an infected copepod as a parasite-
exposed group. ree complete lab tanks with 36 individuals were sampled on each sampling date (30, 60, 90
and 120days post exposure) and ve out of the een sham-exposed individuals per sampling date were ran-
domly selected for CSIA. Sub samples of mosquito larvae were taken once a week, stored at −20°C and pooled
between 1–30, 31–60, 61–90 and 91–120days for CSIA. Dried red mosquito larvae are commercially available
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and a protein rich diet for sh, with a crude protein content of up to 60% and a crude lipid content of around
5%. e AA prole is balanced and adequate for sh farming, although His, Lys or Try can be lacking depend-
ing on the used insect species62, 66, 67. Sticklebacks were starved 72h prior sampling, anesthetized with MS 222
(Sigma-Aldrich, USA) and killed by decapitation. Liver and muscle tissue were collected without skin and bones
and stored at −20°C until further use. Sticklebacks were maintained and treated in accordance with the local
animal welfare authorities and the EU Directive 2010/63/EU for animal experiments. All animal experiments
described were approved by the ‘State Agency for Nature, Environment and Consumer Protection’ (LANUV) of
North Rhine Westphalia, which includes the evaluation by an ethics committee, under the project number 87
51.04.2010.A297. e present study was carried out in compliance with the ARRIVE guidelines (https:// arriv
eguid elines. org/).
Sample preparation. Hydrolysis of amino acids for LC-IRMS analysis has been described in the literature
44, 45, 68. Approximately 5mg of sample material were weighed into 5mL PTFE vials (CEM GmbH, Kamp-Lint-
fort, Germany) and 2.5mL (1:500 ratio of mass to volume) of 6M hydrochloric acid (> 99%, Alfa Aesar, Kandel,
Germany) were added. e vials were closed and kept in a UT 5042 drying oven (Heraeus, Hanau, Germany) at
110°C for 24h. e hydrolysate was ltered (0.2µm PTFE lter), evaporated to dryness under vacuum at 40°C,
reconstituted in 1mL distilled water and ltered again into small 1.5mL HPLC vials. e vials were frozen at
−20°C until LC-IRMS analysis. Glutamine and asparagine are converted to their respective acidic form during
this treatment and measured together with glutamic and aspartic acid. Tryptophan and cysteine are lost during
acid hydrolysis and the amount of methionine was too low to be measured accurately.
LC‑IRMS analysis of AAs. e analysis of individual AAs was performed on a Dionex Ultimate 3000
HPLC Pump (ermo Fisher Scientic, Bremen, Germany) coupled to an IsolinkTM Interface and Delta V
Advantage mass spectrometer (ermo Fisher Scientic, Bremen, Germany). Separation was achieved for 13
AAs (Fig.S1) with a mixed mode cation exchange column (Primesep A, 2.1mm ID, 250mm L, 5µm particle
size) from SIELC, which was in accordance with other studies employing the same separation technique44, 45, 68.
e exact program is described by a study from Raghaven etal. (2010)45 and uses a gradient from mobile phase
A (100% water, pH 7) to mobile phase B (0.3M sulfuric acid, pH 1.5) and column temperature was held at 30°C.
To preserve the HPLC column, which is very sensitive to pH values of over 7, we adjusted the method to start
with water at pH 4 as mobile phase A. eow rate of the mobile phase was set to 260µLmin-1 and oxidation
agents (1.5M orthophosphoric acid and 100 gL-1 disodium peroxodisulfate, Merck, Darmstadt, Germany) were
pumped at 25µLmin-1 each. is resulted in an oxygen background of approximately 12V on the rst cup of
the IRMS, which is the recommended value by the manufacturer to guarantee sucient oxidation conditions.
e experimental units were replicates of pooled dietary samples (n = 3) and biological replicates of stickleback
tissues (liver and muscle, n = 5) for each of the four sampling days taken at day 30, 60, 90 and 120 and the small
sample amount of obtained stickleback tissue did not allow for within-individual replicate analysis. Each hydro-
lyzed sample was injected in triplicate into the LC-IRMS system and outliers were determined by Grubbs tests
on a condence level of 0.95 and excluded from further analysis. We calculated mean values and SD from trip-
licate injections to estimate instrumental precision before referencing our data. Instrumental precision was esti-
mated with an average SD of 0.47‰ for triplicate injections of all AAs, tissues, and sampling days (n = 674). SDs
of triplicate injections of all AAs from either dietary samples (0.51‰, n = 156), liver samples (0.50‰, n = 258)
and muscle samples (0.41‰, n = 260) where almost equal over the complete sampling period. e robustness
of sample preparation and hydrolysis was assessed by conducting replicate analysis (n = 3) of dietary samples,
since these were the only samples providing enough material for multiple replicates. e average SD of replicate
analysis was 0.36‰ and therefore in the same range as triplicate injections. Twelve in-house amino acid stand-
ards (Ala, Asx, Arg, Glx, Gly, His, Lys, Pro, Phe, Ser, r and Tyr) were purchased with a purity of > 98% (Alfa
Aesar, Kandel, Germany) and measured against seven certied international AA reference materials (L-Alanine,
L-Glutamic acid, USGS 64, USGS 66, L-Phenylalanine, L-Proline and L-Valine), purchased from Arndt Schim-
melmann, Department of Earth and Atmospheric Sciences at Indiana University (Bloomington, IN, USA), on an
Isoprime100 Elemental Analyser (Elementar Analysensysteme GmbH, Langenselbold, Germany). A mix of the
in-house standards with a concentration of 100 mgL-1 for each amino acid was regularly measured in between
sample runs and used to directly assign nal isotope values on the VPDB scale. Measurements of pooled AA
in-house standards were further monitored to ensure equal system and method performance throughout the
prolonged measurement periods and the SD of AA standards never exceeded 0.6‰ (N = 34, 17 and 20, respec-
tively; TableS5). is procedure ensures accurate long-term isotope data and follows the identical treatment
procedure69. Five primary RM were measured on the LC-IRMS system before the start of a four week-long
measurement (10 and 5µL injection volumes, each in triplicate). e measured δ13C values of the ve RM were
calibrated with the AA in-house standards and were in good agreement with the true literature values except for
USGS 41, which showed conversion of glutamic acid to pyroglutamic acid and is a known problem even with the
newer USGS 41a material70. Chromatograms were individually checked for proper background and peak detec-
tion. e automated dynamic background detection algorithm from ISODAT 2.0 soware with a block width of
100071 was in many cases able to accurately estimate the background signal, but manual adjustments had to be
made, e.g. for closely eluding AAs or interference of matrix components.
Data analysis. Data analysis was done using Excel from Microso Oce 365 ProPlus (Microso, Red-
mond, Washington, USA), Origin 2019 version 9.60 (OriginLab, Northampton, Massachusetts, USA) and Mat-
lab R2021a (MathWorks Inc., Natick, Massachusetts, USA) with the PLS_Toolbox suite (Eigenvector Research
Inc., Manson, WA). Isotope data are reported as mean δ13CAA values on the VPDB scale in ‰ with its corre-
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sponding standard deviation (TableS1). Data was tested for normality with Kolmogorov–Smirnovtests on a
condence level of 0.95, which was not violated for any given AA and sample. One-way ANOVA was used on
δ13C values of each tissue to test for dierences of AAs between sampling days (xed factor). Using sampling
days as an independent variable in ANOVA can be problematic because it is not strictly categorial, but each of
our sample represents an independent sh individual which couldn’t be sampled multiple times and is there-
fore not a repeated measure. We further conducted ASCA as a multivariate analysis in a design of experiment
approach, with sampling days and tissue types as xed factors and δ13C of each AA as multivariable. ASCA com-
bines the principles of ANOVA and PCA65 and allows to investigate dierences in the isotope signature between
tissues without the inuence of the expected isotope shi over time dominating our analysis. δ13C data for ASCA
was used without preprocessing and the analysis performed with 1000 permutations.
Δδ13C values of individual AAs were rst calculated individually between each stickleback sample (muscle,
liver) and dietary sample and then averaged over all samples on either (1) all days or (2) each sampling day
(TableS3). Δδ13C were pooled for NEAAs and EAAs to test for dierences in trophic fractionation between
those groups among all days. Two-sided t-tests on Δδ13C values of individual AAs were used to test for dier-
ences in trophic fractionation against 0‰ on individual sampling days. e signicance level α was set to 0.01
to compensate for the low number of biological replicates (n = 5 for sh tissue) that were analyzed, and each
ANOVA analysis was accompanied by Brown-Forsythe tests (α = 0.05) to check for equality of group variances
and followed up with Tukey’s post hoc tests to identify signicant dierences between group values. Reducing
the signicance level avoids false-positive results for small sample sizes, but it consequently increases false-
negative results, and we are therefore only discussing the most signicant dierences of our data, while smaller
dierences might be lost.
Data availability
e datasets generated during and/or analysed during the current study are available in the Figshare repository:
https:// doi. org/ 10. 6084/ m9. gsh are. 17014 220. v1.
Received: 9 December 2021; Accepted: 28 June 2022
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Acknowledgements
We thank the Deutsche Forschungsgemeinscha (DFG, German Research Foundation) for funding this project.
Author contributions
All authors contributed substantially to this study. e feeding experiment was designed and conducted by F.F.,
M.N., J.P.S. and J.K.. Isotope analysis was carried out by T.H. and S.K.. Data analysis and evaluation was done
by T.H. with helpful input and support from S.K., M.N., M.A.J and T.C.S.. e manuscript was draed by T.H.
with substantial feedback and help from all other authors.
Funding
Open Access funding enabled and organized by Projekt DEAL.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 022- 15704-7.
Correspondence and requests for materials should be addressed to M.A.J.
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