Takizawa et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00364-w(2020) 7:50

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessA new insight into isotopic fractionation
associated with decarboxylation inorganisms: implications for amino acidisotope approaches in biogeoscience
Yuko Takizawa1* , Yoshinori Takano2, Bohyung Choi1,3,4, Prarthana S. Dharampal5, Shawn A. Steffan5,6,
Nanako O. Ogawa2, Naohiko Ohkouchi2 and Yoshito Chikaraishi1,2

Abstract
Stable nitrogen (15N/14N) and carbon (13C/12C) isotopic compositions of amino acids in organisms have widely been
employed as a powerful tool to evaluate resource utilization and trophic connection among organisms in diverse
ecosystems. However, little is known about the physiological factors or mechanisms responsible for determining
the isotopic discrimination (particularly for carbon) within amino acids of organisms. In the present study, weinvestigated the inter-trophic discrimination of nitrogen and carbon isotopes within amino acids (Δδ15NAA andΔδ13CAA, respectively) using four consumer–diet pairs. Each pairing illustrates a metabolic perspective of isotopic
fractionation of amino acids. The Δδ15NAA values in these combinations reveal a trend consistent with thoseobserved in many other combinations in previous studies. This further validates a standard scenario: thedeamination preferentially removes 14N amino group from diet-derived amino acids, leaving behind the 15Nenriched amino acids in consumer biomass. The Δδ15NAA values thus mirror the activity of amino acid deaminationin consumers. In contrast, the trends in the Δδ13CAA value suggest a different metabolic fate for the amino acidcarbon isotope. Based on our results, we predict the following scenario: decarboxylation preferentially removes 12C
α-carbon (i.e., carbonyl-carbon) from pyruvic acid in glycolysis, and from α-ketoglutaric acid in the tricarboxylic acidcycle, leaving behind the 13C-enriched both pyruvic and α-ketoglutaric acids. The 13C is then transferred to aminoacids that are synthesized from the 13C-enriched precursor molecules within consumers. The Δδ13CAA values
therefore mirror the pathways of de novo amino acid synthesis in consumers. The proposed link between nitrogenand carbon isotopes can refine our knowledge of the potential processes affecting the isotopic fractionation within
diet and consumer compartments, as well as environmental samples.
Keywords: Compound-specific isotope analysis, Amino acids, Nitrogen, Carbon, Trophic discrimination,
Deamination, Decarboxylation, Degradation, Synthesis, Isotope physiology* Correspondence: takizaway@lowtem.hokudai.ac.jp1
Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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Takizawa et al. Progress in Earth and Planetary Science(2020) 7:50

1 Definition and Equations
δ15N, stable isotopic composition of nitrogen;δ 15 N ðpermilÞ ¼ ðð15 N=14 NSample Þ=ð15 N=14 NAIR Þ − 1Þ  1000

δ13C, stable isotopic composition of carbon;
δ 13 C ðpermilÞ ¼ ðð13 C=12 CSample Þ=ð13 C=12 CVPDB Þ − 1Þ  1000

2 Introduction
During the past four decades, the analysis of stable
isotope ratios (e.g., 15N/14N, 13C/12C) has been usedwidely in the study of biogeosciences (e.g., Fry 2006).
While DNA analysis can visualize the biosphere in termsof species through genetic profiles, isotope analysis canvisualize the biosphere in terms of the quantitative fluxin physical and/or biochemical processes (e.g., CarreonMartinez and Heath 2010; Maeda et al. 2012; Traugott
et al. 2013). Isotopic discrimination is frequently quantified by the kinetic isotope effect (α) and flux (F) of theprocesses (e.g., Hoering 1957; Mariotti et al. 1981; Goto
et al. 2018). The wide array of biochemical reactions
within organisms creates diverse and distinctive isotopic
compositions within the biosphere (δ15N, ‰ vs. atmospheric nitrogen gas [AIR]; δ13C, ‰ vs. Vienna Peedee
Belemnite [VPDB]). Often, these stable isotopic compositions represent discernible ratios and thus can be
useful for characterizing and quantifying any given
process in the biosphere (e.g., Hayes 2001; Chikaraishi
2014; Ohkouchi et al. 2015). Identification of key
processes driving isotopic fractionation (ε) is therefore
indispensable for a fundamental understanding of “universality” of the isotopic discrimination (Δ = δA − δB;
where organism A is consumer and B is diet), as well as
for validating the accuracy and precision of isotopic
evidence in application studies (e.g., Hayes 2001; Wadaet al. 2013; Ohkouchi et al. 2015).
Nitrogen isotopes with amino acids undergo varying
degrees of isotopic discrimination (Δδ15NAA) during
trophic transfer (e.g., Gaebler et al. 1963, 1966; Hareet al. 1991; McClelland and Montoya 2002; Chikaraishi
et al. 2007; McCarthy et al. 2007; Popp et al. 2007;
Steffan et al. 2015; McMahon and McCarthy 2016;
Ishikawa 2018). The patterns of Δδ15NAA values havebeen frequently associated with the trophic tendencies
and amino acid degradation pathways within organisms(Takizawa and Chikaraishi 2014, 2017; Takizawa et al.
2017; Choi et al. 2018; Goto et al. 2018). Chikaraishi
et al. (2007) proposed that deamination (precedingtransamination) is a key process for isotopic fractionation of nitrogen within amino acids, which preferentially removes the 14N amino group of diet-derived
amino acids and leaves behind 15N (by up to ~ 3–8‰
per trophic transfer) in the residual pool of amino acids

Page 2 of 13

in consumer biomass. The degree of deamination activity, therefore, results in a great diversity in the isotopic
discrimination–large values for some amino acids (e.g.,alanine, valine, isoleucine, proline, and glutamic acid)and small values for some other amino acids (e.g., methionine and phenylalanine) between consumer anddiet-resource species, as shown in 15N-enrichment factors (Chikaraishi et al. 2009). These two groups of aminoacids are frequently called “trophic” and “source” aminoacids, respectively (e.g., Popp et al. 2007; reviewed in
Ohkouchi et al. 2017). Using this framework, the δ15N
analysis of amino acids has rapidly evolved into a unified
approach: the δ15N values of the source amino acids inorganisms directly provide the δ15N values of primary
producers at bases of food webs, whereas difference inthe Δδ15NAA value between trophic and source aminoacids within a single organism correlates with its position in the trophic hierarchy (e.g., Chikaraishi et al.
2009; Steffan et al. 2013; Chikaraishi et al. 2014; Broek
and McCarthy 2015; Choy et al. 2015; Lorrain et al.
2015; Blanke et al. 2017; Choi et al. 2017; Dharampal
and Findlay 2017; Steffan et al. 2017; Ostrom et al. 2017;
Blanke et al. 2018; Morra et al. 2019).
The large diversity in the carbon isotopic compositionof amino acids (δ13CAA) has been useful in describing
metabolic and trophic interactions (e.g., Abelson and
Hoering 1961; Macko et al. 1987; Ostrom et al. 1994;
Fantle et al. 1999; O’Brien et al. 2002; McCarthy et al.
2004; Larsen et al. 2009, 2013; McMahon et al. 2010,2015; Fry et al. 2018; Takano et al. 2018). Trophic discrimination of carbon isotopes within amino acids(Δδ13CAA) is reportedly small or negligible for aromatic(e.g., phenylalanine) and branched-chain amino acids(e.g., valine, leucine, and isoleucine), but can vary widely
between − 10‰ and + 10‰ for other amino acids (e.g.,
glycine, alanine, aspartic acid, proline, and glutamic acid)(Fantle et al. 1999; Howland et al. 2003; McMahon et al.
2010, 2015). Although there is little knowledge regarding
the key metabolic processes responsible for the observed
Δδ13CAA values (Hayes 2001; Takano et al. 2018), these
two types of amino acids are not consistent with the“trophic” and “source” classification of amino acid nitrogen isotopes. Based on this dichotomy, it has been suggested that the limited amounts of de novo synthesis ofessential amino acids caused the lower values ofΔδ13CAA. Conversely, the large variation in the Δδ13CAA
value for nonessential amino acids is attributed to frequent de novo synthesis reactions and/or metabolicrouting from dietary substrates such as proteins, fats,
and carbohydrates (PFC) in consumers (Fantle et al.
1999; O’Brien et al. 2002, 2005; McMahon et al. 2010;
Choy et al. 2013; McMahon et al. 2015). These findings
provide a framework in which the δ13C values of essential amino acids can potentially serve as a “finger-

(2020) 7:50

Page 3 of 13

printing” tool to trace the trophic legacy of carbon fromdiverse dietary resources (e.g., phytoplankton, macroalgae, bacteria, and fungi) to consumer compartments(e.g., Larsen et al. 2009, 2013, 2015; McMahon et al.
2010, 2016). The identification of the key processes inorganisms thus will be required for better understandingof the metabolic underpinnings of isotope ecology.
In the present study, we determined the Δδ15NAA andΔδ13CAA values together with the concentration andmolar balance of amino acids in four pairs of consumer–
diet invertebrates: (1) sea slug–sponge (collected from acoastal marine ecosystem), (2) ladybug beetle–aphid(collected from a terrestrial ecosystem), (3) green
lacewing–fall armyworm, and (4) green lacewing–green
lacewing (reared in laboratory-controlled feeding experiments). These consumer–diet pairings allow for empirical measurement of inter-trophic discrimination factor(inter-TDF) for the nitrogen and carbon isotopes (i.e.,
Δδ15NAA and Δδ13CAA, respectively) in a single trophic
transfer, and in the process provides a mechanistic
model of the isotopic fractionation processes affecting
the inter-TDF of amino acids.

S. frugiperda larvae were cultured on a single isotopicallyhomogeneous diet and frozen to serve as prey for the C.
rufilabris larvae. Then, a subset of the C. rufilabris larvae
were frozen and used for prey for the remaining C. rufilabris larvae. Four of each trophic group (including 4–8
individuals for each trophic group) were used for the
analysis. The δ15N values of amino acids were reported
in Steffan et al. (2013).

Takizawa et al. Progress in Earth and Planetary Science

3 Materials and Methods
3.1 Natural consumer–diet combinations

The sea slug Hypselodoris festiva and the sponge Halichondria okadai were collected in May 2016, from astony shore in Yugawara (35.080 ° N, 139.070 ° E), Japan.
H. festiva is monophagous on H. okadai (e.g., Watanabe
et al. 2009). Since H. okadai is ubiquitous on near-shore
stones all year, H. festiva was able to feed on this dietyear-round. One individual of H. festiva and one aggregation (ca. 20 mm × 50 mm) of H. okadai were collected,
dried, and homogenized for the isotope analysis. We
note that we did not find other individuals for H. festiva
at the sampling.
Larvae of the ladybug beetle Menochilus sexmaculatus
and larvae of the aphid Aphidoidea sp. were collected in
September 2014, from a shrub at a house-garden in
Yugawara (35.152 ° N, 139.118 ° E), Japan. M. sexmaculatus is aphidophagous in both larval and adult stages(e.g., Khan and Khan 2002), and the larvae of M. sexmaculatus collected in the present study were able to feed
only on a single species of the aphid, Aphidoidea sp.,within a small shrub habitat. Five individuals of M.
sexmaculatus and a colony of approximately fifty
Aphidoidea sp. were collected, dried, and homogenized
for the isotope analysis.
3.2 Reared consumer–diet combinations

The controlled feeding experiments for the two combinations: green lacewing (Chrysoperla rufilabris)–fallarmyworm (Spodoptera frugiperda) and C. rufilabris–C.
rufilabris were conducted in Steffan et al. (2013). Briefly,

3.3 Analysis of nitrogen and carbon isotopic compositionsin amino acids

All samples were prepared for the isotope analysis after
HCl hydrolysis and N-pivaloyl/isopropyl (Pv/iPr) derivatization, according to the procedure in Chikaraishi et al.
(2009). The Pv/iPr derivatization was used for both nitrogen and carbon isotope analysis. However, for carbon
isotope analysis, care was taken to keep reaction balanceof pivaloyl chloride to dried sample (75 μl/mg).
The molar and isotopic compositions of amino acidderivatives were determined by gas chromatography/isotope ratio mass spectrometry (GC/IRMS) using a 6890N
GC (Agilent Technologies) instrument coupled to a DeltaplusXP IRMS instrument through combustion (950 °C)
and reduction (550 °C) furnaces and a countercurrent
dryer (NafionTM) via a GC-C/TC III interface (Thermo
Fisher Scientific). An HP Ultra-2 capillary column (50 m;i.d. 0.32 mm; film thickness 0.52 μm; Agilent Technologies) was used for chromatographic separation of aminoacids. For nitrogen isotope analysis, a liquid nitrogen
CO2 trap was installed between reduction and countercurrent dryer, because the generated CO2 in the combustion furnace was eliminated and the generated N2
was injected to the IRMS instrument. In contrast, forcarbon isotope analysis, a liquid nitrogen CO2 trap was
not installed, and the generated CO2 was injected to the
IRMS instrument.
For both nitrogen and carbon, the combination of sea
slug–sponge and ladybug beetle–aphid was analyzed bytriplicate injections for each sample, and the combination of green lacewing–fall armyworm and green
lacewing–green lacewing was analyzed by a single injection for each of four trophic groups. The δ15N and δ13C
values of amino acid derivatives were obtained as themean ± 1σ, in the triplicate injections for sea slug–
sponge and ladybug beetle–aphid or in the four trophic
groups for green lacewing–fall armyworm and green
lacewing–green lacewing combinations. To assess the reproducibility of the isotope measurement, an isotopic
reference mixture of amino acids (Indiana University;
Shoko Science Co.) was analyzed after every five or six
sample runs, with three pulses of reference N2 or CO2
gas discharged at the beginning and end of each run.
The δ15N and δ13C values were expressed relative to theisotopic compositions of AIR and VPDB, respectively, on(2020) 7:50

Page 4 of 13

scales normalized to known δ values of the referenceamino acids. Analytical errors for the reference mixtures
were 0.3–0.6‰ (1σ for replication) for the δ15N values,and 0.7–1.4‰ (1σ for replication) for the δ13C values.

No substantial difference in the molar balance andconcentration (weight % for total of 7 detected aminoacids) is found between consumers and their diets ina given combination. We found that the molarbalance in the sea slug–sponge combination is different from that in the other combinations. The former
combination has a small abundance of phenylalanine(~ 6.5%) and a high abundance of glycine (~ 30%),
whereas the latter combinations have a small abundance of phenylalanine (6.4–9.6%) and a high abundance of glutamic acid (~ 29%). No substantial
difference (i.e., 5.2% in maximum) in the molarbalance is found within a single combination, which
is much smaller than the analytical error (10%) forthe molar quantification of amino acids in the present
study.

Takizawa et al. Progress in Earth and Planetary Science

3.4 Calculation for Δδ15N and Δδ13C in amino acids

The discrimination of nitrogen isotopes (Δδ15N) of
amino acids in each consumer–diet combination was
calculated by the Eq. (1), because no artificial isotopic
fractionation (εa) is found in experimental procedures(including derivatization) for nitrogen isotopes.
Δδ 15 NA − B ¼ δ 15 Nm;A − δ 15 Nm;Bð1Þ

where δ15Nm indicates the measured δ15N value of a
Pv/iPr amino acid derivative. On the other hand, thediscrimination of carbon isotopes (Δδ13C) of amino acids
was calculated by Eqs. (2) and (3), because artificial
isotopic fractionation (εa) is considerably large in theexperimental procedures (especially for the acylation) for
carbon isotopes. The isotopic mass balance equation forthe δ13C value of an amino acid was given by:no  δ 13 Co ¼ ðno þ nd Þ  δ 13 Cm − nd  δ 13 Cd − εað2Þ

where δ13Cm and δ13Co indicate the measured and original δ13C values of an amino acid, respectively; δ13Cd
and εa indicate the δ13C value of derivative groups andartificial isotopic fractionation during derivatization, respectively; and no and nd indicate the number of carbon
atoms in an amino acid (e.g., two C atoms in glycine)and the derivative groups (e.g., eight C atoms in Pv/iPr
glycine), respectively. The Δδ13C value was given by:Δδ 13 CA − B ¼ δ 13 Co;A - δ 13 Co;B
¼ ½ðno þ nd Þ=no   δ 13 Cm;A − δ 13 Cm;Bð3Þ
Although the δ13Co values were not determined in thepresent study, the Δδ13C values were thus obtained by
Eq. (3).
The Δδ15N and Δδ13C values were reported for sevenamino acids (i.e., phenylalanine, valine, leucine, isoleucine, glycine, alanine, and glutamic acid) in the present
study, because baseline separation for each peak of theamino acids was observed for these seven amino acids
but not for the other amino acids (e.g., aspartic acid,proline, serine, and threonine) on the GC/IRMS
chromatogram.

4.2 Δδ15N and Δδ13C of amino acids

The Δδ15NAA values in these samples range from0.3‰ (for phenylalanine) to 8.1‰ (for glutamic acid),and exhibit a wide diversity in their respective meansand variances among the amino acids examined in
the four consumer–diet pairs (Table 2, Fig. 2a). Thereis a low mean value (0.4‰) with little variation (0.1‰
as 1σ) for an aromatic amino acid (i.e., phenylalanine), a high mean value (from 4.0 to 5.6‰) with a little variation (from 0.7 to 1.0‰) for branched-chainamino acids (i.e., valine, leucine, isoleucine), and alarge mean value (from 2.5 to 7.9‰) with large variation (from 0.2 to 2.8‰) for the other amino acids(i.e., glycine, alanine, and glutamic acid). Glycine
particularly has a unique characteristic for a large
variation (2.8‰) among the four combinations.
The Δδ13CAA values in these samples varies from −1.0 (for valine and leucine) to 17.5‰ (for glycine),
which is approximately twice as much as the variationin the Δδ15NAA value (Table 2, Fig. 2b). There is alow mean value (from − 0.4 to 0.1‰) with a little
variation (from 0.6 to 0.9‰) for an aromatic (i.e.,phenylalanine) and branched-chain (i.e., valine, leucine, and isoleucine) amino acids. On the other hand,
there is a broad range of mean value (from 1.7 to
12.4‰) with variable variation (from 1.1 to 6.8‰) for
the other amino acids (i.e., glycine, alanine, andglutamic acid). The highest mean value (12.4‰) withthe greatest variation (6.8‰) is found for glycine
among the amino acids examined in the present
study.

4 Results
4.1 Concentration and molar balance of amino acids

5 Discussion

Concentration of each amino acid and molar balanceamong these amino acids was determined based on
m/z 28 peak area of chromatograms (Table 1, Fig. 1).

5.1 Analytical issues for Δδ13C of amino acids

Compound-specific isotope analysis of nitrogen and carbon within amino acids has become pervasive in many

Takizawa et al. Progress in Earth and Planetary Science(2020) 7:50

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Table 1 Molar balance of amino acids in the combination samples
Samples

Scientific name

Relative abundance (mol %)
Phe

Val

Leu

Ile

Gly

Ala

Glu

Total contentsof 7 aminoacids (wt %)Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD MeanSD

Natural marine samples
Sponge*Halichondria okadai 6.4

1.4 13.1

1.5 13.2

1.3 11.6

1.7 30.1

3.5 12.5

1.4 13.1

1.7 28.4

3.5

Sea slug**

Hypselodoris festiva

6.6

0.9 13.9

0.9 12.8

2.1 11.7

1.2 29.5

1.2 13.1

1.0 12.4

2.9 28.7

2.3

Aphid*

Aphidoidea sp.

7.4

0.9 10.2

1.2 18.3

1.9 13.8

1.5 12.7

1.3 11.0

1.2 26.6

2.7 31.8

3.0

Ladybug beetle*

Menochilus
sexmaculatus

9.2

0.9 9.1

0.9 21.3

2.1 11.8

1.2 11.1

1.2 9.0

1.0 28.6

2.9 34.2

2.8

Spodoptera
frugiperda

8.1

0.9 13.6

1.5 16.4

1.7 7.4

0.8 13.0

1.7 12.3

3.9 29.3

3.1 33.1

2.1

Green lacewing #1*** Chrysoperlarufilabris

6.4

0.8 11.6

1.2 17.5

1.8 8.6

0.9 12.2

1.2 15.2

1.6 28.5

2.9 34.3

3.6

9.6

1.5 6.4

1.2 17.2

2.1 5.9

0.8 15.9

2.3 14.7

2.0 30.3

4.1 34.5

4.2

Natural terrestrial samples

Reared samples
Fall armyworm***

Green lacewing #2***
Chrysoperla rufilabris* SD represents 1σ for comparing 3 injections for 1 aggregation (sponge), 1 colony (aphid), and 5 individuals (ladybug beetle)** SD represents 1σ for comparing 3 injections for 1 individual (sea slug)*** SD represents 1σ for comparing 1 injection for each of 3 trophic groups (including 4-7 individuals for each group)

Fig. 1 Molar balance of amino acids for four combinations of consumer and diet. Error bar indicates standard deviation (1σ) of replicate measurements

(2020) 7:50

Takizawa et al. Progress in Earth and Planetary Science

Page 6 of 13

Table 2 Nitrogen and carbon isotopic discriminations on amino acids in the four combination samples
Consumer - diet combination

Δ (δconsumer − δdiet) value (‰)
Phe

Val

Leu

Ile

Gly

Ala

Glu

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Sea sulg - Sponge*

0.5

0.3

4.8

0.6

3.0

0.6

5.6

0.4

1.6

0.6

5.5

0.6

7.9

0.3

Ladybug beetle - Aphid**

0.3

0.3

6.5

0.5

5.3

0.5

3.6

0.6

6.6

0.5

4.5

0.5

7.7

0.3

Δδ15N (‰)

Green lacewing #1 - Fall armyworm ***

0.4

0.3

5.5

0.3

3.6

0.5

3.3

0.4

0.8

0.7

5.4

0.4

8.0

0.4

Green lacewing #2 - Green lacewing #1***

0.5

0.3

5.5

0.3

4.2

0.4

3.8

0.5

0.8

0.3

5.5

0.4

8.1

0.5

Sea sulg - Sponge*

-0.3

0.8

0.0

0.7

0.4

0.7

0.4

0.9

11.9

0.6

3.9

0.4

3.5

0.5

Ladybug beetle - Aphid**

-0.5

0.8

-1.0

0.8

-1.0

0.9

-0.4

0.6

3.0

1.1

4.9

0.8

2.2

0.2

Green lacewing #1 - Fall armyworm ***

-0.6

1.1

-0.9

0.5

0.3

0.2

-0.2

0.6

17.3

0.5

2.8

0.3

1.0

0.2

Green lacewing #2 - Green lacewing #1 ***

0.2

1.1

0.3

0.9

-0.5

0.5

0.5

0.9

17.5

0.7

2.6

0.4

0.2

0.9

Δδ13C (‰)* SD represents 1σ propagation error for comparing 1σ of Sea slug (3 injections for 1 individual) and Sponge (3 injections for 1 aggregation)** SD represents 1σ propagation error for comparing 1σ of Ladybug beetle (3 injections for 5 individuals) and Aphid (3 injections for 1 colony)*** SD represents 1σ propagation error for comparing 1σ of consumer and diet (1 injections for 4 trophic groups)

Fig. 2 a Δδ15N and b Δδ13C values of amino acids in the
consumer–diet combinations for sea slug feeds on sponge (SS),ladybug beetle feeds on aphid (LA), green lacewing feeds on fallarmyworm (GF), and green lacewing feeds on green lacewing (GG).
Error bar indicates standard deviation (1σ) of
replicate measurements

branches of science following the commercial production of the gas chromatograph-isotope ratio mass spectrometer (GC-IRMS) in the 1990s. This system has been
carried out via derivatization to neutralize polar carboxyl(-COOH), amino (-NH2), and hydroxyl (-OH) groups inamino acids by less-polar moieties prior to the GCIRMS analysis (e.g., Engel et al. 1990; Silfer et al. 1991;
Demmelmair and Schmidt 1993; Merritt and Hayes
1994; Metges et al. 1996; Chikaraishi et al. 2010a). However, this technique is still in developmental stages particularly for carbon isotope analysis of amino acids due
to associated methodological challenges (e.g., Metgesand Daenzer 2000; Docherty et al. 2001; Corr et al.
2007a, 2007b; Chikaraishi and Ohkouchi 2010). During
the derivatization, the acylation of the amino group generates an artificial isotopic fractionation (εa) on the
carbonyl-carbon of acyl moiety in the derivatives, because the carbon atom in derivatization reagents bondwith amino acids in a non-quantitative reaction (Silfer
et al. 1991; Rieley 1994; Metges and Daenzer 2000;
Docherty et al. 2001; Chikaraishi and Ohkouchi 2010).
This results in three methodological issues: (1) aminoacids have large diversity in the structural characteristic(e.g., carbon skeleton, functional group, reaction points,
polarity, etc.), resulting in diverse kinetic isotope effects;(2) the εa value are strongly dependent on the molar balance between amino acids and derivative reagents; and(3) the εa values are driven by the molar balance among
amino acids, as well as on the sample matrix (particularly the content of hydroxyl groups) (Chikaraishi and
Ohkouchi 2010). These issues can cause unreliable determination of the δ13CAA values by GC-IRMS analysis(e.g., Silfer et al. 1991; Chikaraishi and Ohkouchi 2010;
Dunn et al. 2011). Even though the issues of isotope effect and molar balance of given amino acids can be(2020) 7:50

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calculated and controlled (Corr et al. 2007a, 2007b), thecontrol (or correction) of the εa values derived from thecontribution of matrix is difficult to ascertain in manycases (Chikaraishi and Ohkouchi 2010). For instance,
there is a high dissimilarity in the PFC composition between plants (i.e., cellulose-based) and herbivore (i.e.,
protein-based) tissues. This results in a large disparity ofthe εa values for individual amino acids between samples
derived from producers and herbivores (Chikaraishi and
Ohkouchi 2010; Dunn et al. 2011). To avoid these methodological issues concerning the δ13CAA measurement,
we used four carefully selected animal–animal combinations such that the molar balance of amino acids is comparable across consumers and diets, and this was later
confirmed by direct quantification (Table 1, Fig. 1). Furthermore, each combination was designed such that the
sample matrixes are comparable across diet and consumer compartments. As a result, the uncertainty (i.e.,
precision) in the Δδ13CAA value obtained from the analysis of each combination was better than 0.8–3.3‰, thepropagation of analytical precision for δ13C measurement being 0.7–1.4‰.

effect (α) and flux (F) of the processes that either formor cleave chemical bonds in metabolic pathways oforganisms (e.g., Hoering 1957; Mariotti et al. 1981;
Hayes 2001; Chikaraishi 2014; Ohkouchi et al. 2015;
Goto et al. 2018). In the case of nitrogen, the Δδ15NAA
values in consumer–diet combination can be explainedby the activity of deamination for the “source” (e.g.,phenylalanine) and “trophic” (e.g., valine, leucine,isoleucine, alanine, and glutamic acid) amino acids,
respectively (Fig. 3a) (Chikaraishi et al. 2009, 2010b;
Steffan et al. 2015; McMahon and McCarthy 2016;
Ohkouchi et al. 2017). Consistency of the Δδ15NAA
values between this and previous studies further
validates this traditional interpretation.
The Δδ13CAA values in consumer–diet combination
have been insufficiently understood in previous studies.
Four decades ago, DeNiro and Epstein (1977) first demonstrated that metabolic products (i.e., lipids) are greatly
depleted in 13C (by 7–8‰) for E. coli cultured withglucose or pyruvic acid, but less depleted in 13C (only by
1‰) for that with acetate. This suggested that the
carbon isotopic fractionation is closely related to the decarboxylation of pyruvic acid to form acetyl-CoA. They
further proposed that the decarboxylation of pyruvic
acid causes the depletion in 13C at the position of the
carbonyl-carbon on acetyl-CoA, likely due to the preferential cleaving of the 12C-12C bond on the pyruvic acidin the decarboxylation (e.g., Monson and Hayes 1980,
1982; Melzer and Schmidt 1987: Chikaraishi 2014).
Based on the findings from prior studies, we propose thefollowing framework (Fig. 3b) to interpret the Δδ13CAA
values observed in the present study:

Takizawa et al. Progress in Earth and Planetary Science

5.2 Trophic isotopic discrimination (Δδ15N and Δδ13C) of
amino acids

Based on the isotopic discriminations observed, theseven amino acids investigated are classified into the following three groups (Fig. 2): (1) no significant fractionation, with near-zero values of both Δδ15N and Δδ13C
values for the aromatic amino acid (i.e., phenylalanine);(2) a small positive fractionation of the Δδ15N values,
but no fractionation of the Δδ13C values for the
branched-chain amino acids (i.e., valine, leucine, and isoleucine); and (3) a large positive fractionation for both
Δδ15N and Δδ13C values for the other amino acids (i.e.,
glycine, alanine, and glutamic acid). Trends in the isotopic discrimination for the first two groups in thepresent study are consistent with those reported in previous studies (e.g., Chikaraishi et al. 2009; McMahon
et al. 2010, 2015; Steffan et al. 2015). On the other hand,
we found positive Δδ13C values in the last group, whichis consistent with the values found in McMahon et al.
(2015). However, several previous studies reported abroad range of positive and negative values for theΔδ13C values (e.g., Fantle et al. 1999; Howland et al.
2003; McMahon et al. 2010). Although specific reasonsfor such conflicting results are unknown, they may be
attributed to the methodological issues concerning thecarbon isotopic composition of amino acids.
5.3 Physiological mechanisms for the isotopic
discrimination (Δ)

The kinetic isotopic fractionation (ε) within organic
compounds is principally controlled by the isotope(1) The decarboxylation of pyruvic acid (Pyr) inglycolysis preferentially eliminates 12C as acetylCoA, leaving behind the enriched 13C in theresidual pool of pyruvic acid.
(2) Similarly, the decarboxylation of α-ketoglutaric acid
(α-Kg) in the tricarboxylic acid (TCA) cycle
preferentially eliminates 12C as succinyl-CoA (SucCoA), leaving behind the enriched 13C in the residual pool of α-ketoglutaric acid.
(3) The enrichment of 13C in the residual pyruvic and αketoglutaric acids is eventually transferred to aminoacids, when the consumers produce the amino acids
through de novo synthesis (or metabolic routing)from these 13C-enriched residual acids.
According to these processes, the trophic discriminationof carbon isotopes (Δδ13C) is not found in “essential”
amino acids, where synthetic flux is limited (i.e., phenylalanine, valine, leucine, and isoleucine in the present
study). Since decarboxylation in glycolysis and TCA cycle
elevates 13C content of the synthetic precursors of amino

Takizawa et al. Progress in Earth and Planetary Science(2020) 7:50

Page 8 of 13

Fig. 3 Simplified metabolic map of degradation and synthetic networks of the amino acids (i.e., glycine, alanine, valine, leucine, isoleucine,
glutamic acid, and phenylalanine) with respect to isotopic fractionation: a 15N/14N associated with deamination of amino acids and b 13C/12C
associated with decarboxylation of pyruvic acid (Pyr) and α-ketoglutaric acid (α-Kg). 15N can be enriched in the six amino acids (i.e., glycine,
alanine, valine, leucine, isoleucine, and glutamic acid), and 13C can be enriched in the three amino acids (i.e., glycine, alanine, and glutamic acid)and their precursors on the pathway

acids, the de novo synthesized nonessential amino acids
incorporate the 13C into the newly produced amino acids
in consumers, resulting in large Δδ13CAA values (i.e., glycine, alanine, and glutamic acid in the present study).
5.4 Key processes affecting the isotopic fractionation (ε)

deamination and decarboxylation initiate the formationof C-N and C-C bonds between substrate and enzyme,
respectively, and ultimately cleave a chemical bond on
the substrate. We therefore speculate that the “key” reactions responsible for the isotopic fractionation of aminoacids proceeds in the following sequential pattern:

From a theoretical perspective, the preferential “cleaving” of chemical bond between light isotopes (i.e.,
12
C-14N) compared to between light and heavy isotopes(i.e., 12C-15N and 13C-14N) of amino acids is the dominant process in the isotopic fractionation of amino acids
in metabolic pathways (e.g., Chikaraishi et al. 2007,2009). This leads to a pattern of simultaneous enrichment in 15N and 13C associated with deamination ofamino acids. However, the results in the present study
offer little support to this theoretical consideration andsuggest that the Δδ15NAA values are independent of theΔδ13CAA values (particularly for branched-chain aminoacids) in the studied consumers (Fig. 2). Therefore, wesuggest that the isotopic fractionation (ε) is attributable
to the alternative processes that involve the preferential“forming” of the 12C-14N or 12C-12C bond. Indeed, the(1) During “forming” of the C-N double bond between
an amino acid and pyridoxal phosphate (PLP) at theinitial step of deamination, 14N amino group inamino acids is preferentially bonded and deaminated, and this reaction leaves behind 15N in theresidual pool of amino acids in consumer biomass(Fig. 4a).
(2) During “forming” of the C-C bond between pyruvic
acid (or α-ketoglutaric acid) and thiamine
pyrophosphate (TPP) at the initial step of decarboxylation, the 12C α-carbon in pyruvic acid (or αketoglutaric acid) is preferentially bonded with the
TPP and then decarboxylated from the neighboringcarboxyl group carbon in pyruvic acid, resulting inthat this reaction leaves behind 13C in the residual

Takizawa et al. Progress in Earth and Planetary Science(2020) 7:50

Page 9 of 13

Fig. 4 a Possible scenarios of the isotopic fractionation during deamination of amino acids and b decarboxylation of pyruvic and α-ketoglutaric
acids: the fractionation is caused by forming of C-N or C-C bond. Asterisk indicates atoms where isotopic fractionation occurs. Yellow highlightindicates reaction points accompanied with the isotopic fractionation

pool of pyruvic acid (or α-ketoglutaric acid) in consumer biomass (Fig. 4b). The enriched 13C can betransferred to those amino acids that are subsequently synthesized de novo by the consumers.

6 Implications
Compound-specific isotope analysis of amino acids has
been widely used in food web ecology. While nitrogenisotopes have been used to estimate the trophic position(TP) of organisms, carbon isotopes have proven usefulin tracing the producers at the foundations of food webs
(reviewed in Ohkouchi et al. 2017). However, possible
factors for trophic discrimination (Δ) and key reactions
responsible for isotopic fractionation (ε) of amino acids
cannot be sufficiently explained by isotopic fractionation
caused by the cleaving of chemical bond. Moreover, artificial isotopic fractionation (εa) during the derivatizationof amino acids increases uncertainty on the δ13CAA value
observed.

Through careful selection of consumer–diet pairings,
we were able to minimize the methodological error in
the Δδ13CAA values, and delineate possible factors driving the isotopic discrimination and fractionation ofamino acids (Table 3). The isotopic fractionation (ε) canbe explained by proposed mechanisms involving thepreferential formation of the 12C-14N bond (i.e., aminoacids with PLP) and 12C-12C bond (i.e., amino acidprecursors with TPP) (Fig. 4). In the case of nitrogenisotopes, although deamination of amino acids is a key
reaction (Chikaraishi et al. 2007), the results in thepresent study further reveal that balance between
degradation and synthetic flux is a dominant factor forcontrolling the trophic isotopic discrimination (Table 3).
In the case of carbon isotopes, the results in the present
study reveal that decarboxylation of the precursor molecules (e.g., pyruvic and α-ketoglutaric acids) is a key reaction, and that the resulting de novo synthetic flux isthe dominant factor controlling trophic isotopic

Takizawa et al. Progress in Earth and Planetary Science(2020) 7:50

Page 10 of 13

Table 3 Possible factors for controlling trophic discrimination (Δ) and key reactions responsible for isotopic fractionation (ε) on
consumers
Possible factor for Δ of an amino acid
NitrogenKey reaction responsible for ε of an amino acid

Classificationof amino
acids

CarbonNitrogenCarbonAromatic -

-

-

-

Source-Essential

Branched Deamination flux of the amino acid-

Deamination of
the amino acid-

Trophic-Essential

OthersBiosynthetic flux of Deamination of
the amino acidthe amino acid

Decarboxylation of the precursor (e.g.,
pyruvic acid and α-ketoglutaric acid)

Trophic-Nonessential

Balance between deamination andbiosynthetic flux of the amino aciddiscrimination (Table 3). These key reactions form abiochemical intersection of amino acid metabolism, linking the isotope physiology of amino acids in organisms.
We use a cross-plot between Δδ15N and Δδ13C to represent this intersection to classify amino acids into threegroups (Table 3, Fig. 5):(1) Source-essential: The aromatic amino acid (i.e.,phenylalanine) has the Δδ15N and Δδ13C values
very close to zero, due to an absence of bothdeamination and synthesis activities in consumers(Fig. 5a).
(2) Trophic-essential: The branched-chain amino acids(i.e., valine, leucine, and isoleucine) have highΔδ15N, but near-zero Δδ13C values, due to the presence of deamination and an absence of synthesis activities in consumers, respectively (Fig. 5b).
(3) Trophic-nonessential: The other amino acids (i.e.,
glycine, alanine, and glutamic acid) have an inverse
correlation between the Δδ15NAA and Δδ13CAA
values, due to varying degrees of involvement indeamination and synthesis in consumers (Fig. 5c).
The diversity and variation in the trophic isotopic
discrimination among amino acids are closely related toFig. 5 Possible classification of amino acids: a source-essential, b
trophic-essential, and c trophic-nonessential on the cross-plot of theΔδ15N and Δδ13C values

physiological status of organisms. Balance between
degradation and synthesis of amino acids shifts based on
the energy requirement with respect to environmental
conditions such as satiation versus starvation, PFC
balance in diets, and life strategy specific to organisms(Germain et al. 2013; Chikaraishi et al. 2015; McMahon
et al. 2015; Steffan et al. 2013, 2015; Takizawa et al.
2017; Choi et al. 2018).
The proposed bridged perspective between theΔδ15NAA and Δδ13CAA values can reveal the potential
processes affecting the isotopic compositions of ecological and environmental samples. While the near-zerovalues of Δδ13C of essential amino acids (e.g., phenylalanine, valine, leucine, and isoleucine) have been used
to characterize the contribution from basal resources toecosystems, the broader basis of such empirical dataremained largely unknown (Larsen et al. 2009, 2013,2015; McMahon et al. 2010, 2016). Our findings suggestthat such observation arises from the fact that essential
amino acids are largely diet-derived, leaving limited
scope for the incorporation of decarboxylation-derived
13
C-enriched precursor molecules (Fig. 2). Additionally,
there is little information explaining the idiosyncratic
behavior of specific amino acids. For instance, there is alarge variation in the Δδ15N value of glycine (e.g., 4.0 ±3.5‰ in Chikaraishi et al. 2010b, and 3.9 ± 4.9 in
McMahon and McCarthy 2016), and the Δδ15N valuesof glutamic acid have been frequently compressed inseveral animals, particularly for high trophic level consumers that have urea/uric acid cycle (e.g., McMahon
and McCarthy 2016). The inverse relationship withrespect to Δδ15NAA and Δδ13CAA values for trophicnonessential amino acids, glycine, alanine, and glutamic
acid, observed in the present study can potentiallyexplain this significantly large variation. Increasing frequency of deaminating reactions expands the Δδ15NAA
values, and lowered influx of de novo-synthesized aminoacids compresses the Δδ13CAA values. Conversely, increased abundance of de novo-synthesized amino acids
expands Δδ13CAA values, and this is accompanied by theconcomitant compression of the Δδ15NAA values. The
combined analysis of the Δδ15N and Δδ13C values ofTakizawa et al. Progress in Earth and Planetary Science(2020) 7:50

individual amino acids can thus reflect the metabolicstatus of consumers and offer a more holistic view oftrophic interactions.
The main limitation associated with δ13CAA andΔδ13CAA analysis (e.g., Chikaraishi and Ohkouchi 2010)
arises from the artificial isotopic fractionation (εa in Eqs.
2 and 3) introduced during amino acids derivatization.
This methodological challenge has limited the scope of
empirical quantification of isotopic fractionation (ε) associated with “key” processes in metabolism. Continual
efforts directed towards method development (e.g.,
Metges and Daenzer 2000; Docherty et al. 2001; Corret al. 2007a, 2007b; Smith et al. 2009; Chikaraishi and
Ohkouchi 2010) will improve the utility of amino acidisotope approaches in the biogeoscience.
Abbreviations
AA: Amino acid; DNA: Deoxyribonucleic acid; AIR: Atmospheric nitrogen; Pv/
iPr derivatization: Pivaloyl/isopropyl derivatization; GC/IRMS: Gas
chromatography/isotope ratio mass spectrometry; GC-IRMS: Gas
chromatograph-isotope ratio mass spectrometer; VPDB: Vienna Peedee
Belemnite; SS: Sea slug–sponge; LA: Ladybug beetle–aphid; GF: Green
lacewing–fall armyworm; GG: Green lacewing–green lacewing; PFC: Protein–fat–carbohydrate; Pyr: Pyruvic acid; 3PG: 3-Phosphoglyceric acid;
PEP: Phosphoenolpyruvic acid; α-Kg: α-Ketoglutaric acid; Suc-CoA: SuccinylCoA; PLP: Pyridoxal phosphate; TCA: Tricarboxylic acid; TPP: Thiamine
pyrophosphate
Acknowledgements
We greatly appreciate Dr. Eitaro Wada for his lectures, expert comments/
advices, and constructive discussion, which deconvoluted complex issues inthe study of stable isotope physiology and ecology. We also appreciate Dr.
Naoto F. Ishikawa (JAMSTEC), Prof. Rong Fan (Northwest University), and Mr.
Daochao Xing (Hokkaido University) for the discussion of isotope ratios inamino acids and those ecological aspects. Two anonymous reviewers are
greatly acknowledged for improving the manuscript. We thank to the young
researcher travel grant (Y. Takizawa) of the Biogeoscience section (FY2017) inJapan Geoscience Union (JpGU), which gave us an opportunity to write thispaper in Progress in Earth and Planetary Science.
Authors’ contributions
Y.Takizawa, Y.Takano, and Y.C. conceived the present study. Y.C. collectednatural samples and S.A.S. provided reared samples. The manuscript was
written primarily by Y.Takizawa with contribution from Y.C., B.C., P.S.D., S.A.S.,
Y.Takano, N.O., and N.O.O. Y.Takizawa and Y.C prepared and analyzed aminoacid derivatives. The authors read and approved the final manuscript.
Funding
This work was supported by JSPS KAKENHI Grant Number JP 16 J01704 to Y.
Takizawa, Grant-in-Aid for Scientific Research of the JSPS to Y. Chikaraishi(No. 17H02988), Y. Takano (No. 16H04083), and N. Ohkouchi (No. 16H02236),
USDA-ARS appropriated funds to S.A. Steffan, as well as funds from Silicone
Industry Association of Japan to Y. Chikaraishi.
Availability of data and materials
The data for this paper are tabulated in this manuscript.
Competing interests
We have no conflicts of interests and all authors have seen this manuscript
and agree to this submission.
Author details
1
Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan.
2
Biogeochemistry Research Center (BGC), Japan Agency for Marine-Earth
Science and Technology, Yokosuka, Japan. 3Department of Marine Sciences
and Convergent Technology, Hanyang University, Ansan, Republic of Korea.
4
Fisheries Science Institute, Chonnam University, Yeosu, Republic of Korea.

Page 11 of 13

5

Department of Entomology, University of Wisconsin, Madison, WI, USA. 6US
Department of Agriculture, Agricultural Research Service, Madison, WI, USA.
Received: 3 April 2020 Accepted: 19 August 2020

References
Abelson PH, Hoering TC (1961) Carbon isotope fractionation in formation ofamino acids by photosynthetic organisms. Proc Natl Acad Sci 47:623–632
Blanke C, Chikaraishi Y, Vander Zanden MJ (2018) Historical niche partitioning
and long-term trophic shifts in Laurentian Great Lakes deepwater
Coregonids. Ecosphere 9:e02080
Blanke CM, Chikaraishi Y, Takizawa Y, Steffan SA, Dharampal PS, Vander Zanden
MJ (2017) Comparing compound-specific and bulk stable nitrogen isotope
trophic discrimination factors across multiple freshwater fish species anddiets. Can J Fish Aquat Sci 74:1291–1297
Broek TAB, McCarthy MD (2015) A new approach to δ15N compound-specificamino acid trophic position measurements: preparative high pressure lipid
chromatography technique for purifying under derivatized amino acids forstable isotope analysis. Limnol Oceanogr Methods 12:840–852
Carreon-Martinez L, Heath DD (2010) Revolution in food web analysis andtrophic ecology: diet analysis by DNA and stable isotope analysis. Mol Ecol
19:25–27
Chikaraishi Y (2014) 13C/12C signatures in plants and algae. In: Falkowski PG,
Freeman KH (eds) Treatise on geochemistry 2nd edition, Volume 12,
Amsterdam, pp 95–123
Chikaraishi Y, Kashiyama Y, Ogawa NO, Kitazato H, Ohkouchi N (2007)
Biosynthetic and metabolic controls of nitrogen isotopic composition ofamino acids in marine macroalgae and gastropods: implications for aquatic
food web studies. Mar Ecol Prog Ser 342:85–90
Chikaraishi Y, Ogawa NO, Kashiyama Y, Takano Y, Suga H, Tomitani A, Miyashita
H, Kitazato H, Ohkouchi N (2009) Determination of aquatic food-web
structure based on compound-specific nitrogen isotopic composition ofamino acids. Limnol Oceanogr Methods 7:740–750
Chikaraishi Y, Ogawa NO, Ohkouchi N (2010b) Further evaluation of the trophic
level estimation based on nitrogen isotopic composition of amino acids. In:
Ohkouchi N, Tayasu I, Koba K (eds) Earth, Life, and Isotopes. Kyoto University
Press, Kyoto, pp 37–51
Chikaraishi Y, Ohkouchi N (2010) An improved method for precise determinationof carbon isotopic composition of amino acids. In: Ohkouchi N, Tayasu I,
Koba K (eds) Earth, Life, and Isotopes. Kyoto University Press, Kyoto, pp 355–
366
Chikaraishi Y, Steffan SA, Ogawa NO, Ishikawa FI, Sasaki Y, Tsuchiya M, Ohkouchi
N (2014) High-resolution food webs based on nitrogen isotopic compositionof amino acids. Ecol Evol 4:2423–2449
Chikaraishi Y, Steffan SA, Takano Y, Ohkouchi N (2015) Diet quality influences
isotopic discrimination among amino acids in an aquatic vertebrate. Ecol
Evol 5:2048–2059
Chikaraishi Y, Takano Y, Ogawa NO, Ohkouchi N (2010a) Instrumental
optimization for compound-specific nitrogen isotope analysis of amino acids
by gas chromatography/combustion/isotope ratio mass spectrometry. In:
Ohkouchi N, Tayasu I, Koba K (eds) Earth, Life, and Isotopes. Kyoto University
Press, Kyoto, pp 367–386
Choi B, Ha S-Y, Lee J-S, Chikaraishi Y, Ohkouchi N, Shin K-H (2017) Trophic
interaction among organisms in a seagrass meadow ecosystem as revealedby bulk δ13C and amino acid δ15N analyses. Limnol Oceanogr 62:1426–1435
Choi B, Takizawa Y, Chikaraishi Y (2018) Compression of trophic discrimination in
15 14
N/ N within amino acids for herbivorous gastropods. Res Organic
Geochem 34:29–35
Choy CA, Popp BN, Hannides CCS, Drazen JC (2015) Trophic structure and foodresources of epipelagic and mesopelagic fishes in the North Pacific
Subtropical Gyre ecosystem inferred from nitrogen isotopic compositions.
Limnol Oceanogr 60:1156–1171
Choy K, Nash SH, Kristal AR, Hopkins S, Boyer BB, O’Brien M (2013) The carbon
isotope ratio of alanine in red blood cells is a new candidate biomarker ofsugar-sweetened beverage intake. J Nutr 143:878–884
Corr LT, Berstan R, Evershed RP (2007a) Optimisation of derivatisation proceduresfor the determination of δ13C values of amino acids by gas chromatography/
combustion/isotope ratio mass spectrometry. Rapid Commun Mass
Spectrom 21:3759–3771

(2020) 7:50

Page 12 of 13

Corr LT, Berstan R, Evershed RP (2007b) Development of N-acetyl methyl ester
derivatives for the determination of δ13C values of amino acids using gas
chromatography-combustion-isotope ratio mass spectrometry. Anal Chem
79:9082–9090
Demmelmair H, Schmidt H-L (1993) Precise δ13C-determination in the range ofnatural abundance on amino acids from protein hydrolysates by gas
chromatography - isotope ratio mass spectrometry. Isot Environ Health Stud
29:237–250
DeNiro MJ, Epstein S (1977) Mechanism of carbon isotope fractionation
associated with lipid synthesis. Science 197:261–263
Dharampal PS, Findlay RH (2017) Mercury levels in largemouth bass (Micropterus
salmpides) from regulate and unregulated rivers. Chemosphere 170:134–170
Docherty G, Jones V, Evershed RP (2001) Practical and theoretical considerationsin the gas chromatography/combustion/isotope ratio mass spectrometry
δ13C analysis of small polyfunctional compounds. Rapid Commun Mass
Spectrom 15:730–738
Dunn PJH, Honch NV, Evershed RP (2011) Comparison of liquid chromatographyisotope ratio mass spectrometry (LC/IRMS) and gas chromatographycombustion-isotope ratio mass spectrometry (GC/C/IRMS) for thedetermination of collagen amino acid δ13C values for palaeodietary andpalaeoecological reconstruction. Rapid Commun Mass Spectrom 25:2995–
3011
Engel MH, Macko SA, Silfer JA (1990) Carbon isotope composition of individual
amino acids in the Murchison meteorite. Nature 348:47–49
Fantle MS, Dittel AI, Schwalm SM, Epifanio CE, Fogel ML (1999) A food web
analysis of the juvenile blue crab, Callinectes sapidus, using stable isotopes inwhole animals and individual amino acids. Oecologica 120:416–426
Fry B (2006) Stable isotope ecology. Springer, New YorkFry B, Carter JF, Yamada K, Yoshida N, Juchelka D (2018) Position-specific 13C/12C
analysis of amino acid carboxyl groups – automated flow-injection analysis
based on reaction with ninhydrin. Rapid Commun Mass Spectrom 32:992–1000
Gaebler OH, Choitz HC, Vitti TG, Vukmirovich R (1963) Significance of 15N excess
in nitrogenous compounds of biological origin. Can J Biochem Physiol 41:
1089–1097
Gaebler OH, Vitti TG, Vukmirovich R (1966) Isotope effects in metabolism of 14N and15
N from unlabeled dietary proteins. Can J Biochem Physiol 44:1249–1275
Germain LR, Koch PL, Harvey J, McCarthy MD (2013) Nitrogen isotope
fractionation in amino acids from harbor seals: implications for compoundspecific trophic position calculations. Mar Ecol Prog Ser 482:265–277
Goto AS, Miura K, Korenaga T, Hasegawa T, Ohkouchi N, Chikaraishi Y (2018)
Fractionation of stable nitrogen isotopes (15N/14N) during enzymatic
deamination of glutamic acid: Implications for mass and energy transfers inthe biosphere. Geochem J 52:273–280
Hare PE, Fogel ML, Stafford TW Jr, Mitchell AD, Hoering TC (1991) The isotopic
composition of carbon and nitrogen in individual amino acids isolated frommodern and fossil proteins. J Archaeol Sci 18:211–292
Hayes JM (2001) Fractionation of carbon and hydrogen isotopes in biosynthetic
processes. In: Valley JW, Cole DR (eds) Reviews in Mineralogy andGeochemistry 43, Stable Isotope Geochemistry. The Mineralogical Society ofAmerica, Washington, pp 225–277
Hoering T (1957) The isotopic composition of the ammonia and the nitrate ion
in rain. Geochim Cosmochim Acta 12:97–102
Howland MR, Corr LT, Young SMM, Jones V, Jim S, Van Der Merwe NJ, MitchellAD, Evershed RP (2003) Expression of the dietary isotope signal in thecompound-specific δ13C values of pig bone lipids and amino acids. Int J
Osteoarchaeol 13:54–65
Ishikawa NF (2018) Use of compound-specific nitrogen isotope analysis in trophic
ecology: assumptions, applications, and implications. Ecol Res 33:825–837
Khan MR, Khan MR (2002) Mass rearing of Menochilus sexmaculatus Fabricus(Coleoptera: Coccinellidae) on natural and artificial diets. Int J Agric Biol 4:
107–109
Larsen T, Bach LT, Salvatteci R, Wang YV, Andersen N, Ventura M, McCarthy MD(2015) Assessing the potential of amino acid 13C patterns as a carbon source
tracer in marine sediments: effects of algal growth conditions andsedimentary diagenesis. Biogeoscience 12:4979–4992
Larsen T, Taylor DE, Leigh MB, O’Brien DM (2009) Stable isotope fingerprinting: a
novel method for identifying plant, fungal, or bacterial origins of aminoacids. Ecology 90:3526–3535
Larsen T, Ventura M, Andersen N, O’Brien DM, Piatkowski U, McCarthy MD (2013)
Tracing carbon sources through aquatic and terrestrial food webs using
amino acid stable isotope fingerprinting. PLOS ONE 8:e73441

Lorrain AL, Graham BS, Popp BN, Allain V, Olson RJ, Hunt BPV, Potir M, Fry B,
Galván-Magaña F, Menkes CER, Kaehler S, Ménard F (2015) Nitrogen isotopic
baselines and implications for estimating foraging habitat and trophic
position of yellowfin tuna in the Indian and Pacific Oceans. Deep-Sea Res II
Top Stud Oceanogr 113:188–198
Macko SA, Fogel ML, Hare PE, Hoering TC (1987) Isotopic fractionation of
nitrogen and carbon in the synthesis of amino acids by microorganisms.
Chem Geol 65:79–92
Maeda T, Hirose E, Chikaraishi Y, Kawato M, Takishita K, Yoshida T, Verbruggen H,
Tanaka J, Shimamura S, Takaki Y, Tsuchiya M, Iwai K, Maruyama T (2012)
Algivore or phototroph? Plakobranchus ocellatus (Gastropoda) continuously
acquires kleptoplasts and nutrition from multiple algal species in nature.
PLoS ONE 7:e42024
Mariotti A, Germon JC, Hubert P, Kaiser P, Letolle R, Tardieux A, Tardieux P (1981)
Experimental determination of nitrogen kinetic isotope fractionation: someprinciples; illustration for the denitrification and nitrification processes. Plant
Soil 62:413–430
McCarthy MD, Benner R, Lee C, Fogel ML (2007) Amino acid nitrogen isotopic
fractionation patterns as indicators of heterotrophy in plankton, particulate,and dissolved organic matter. Geochim Cosmochim Acta 71:4727–2744
McCarthy MD, Benner R, Lee C, Hedhes JI, Fogel ML (2004) Amino acid carbon
isotopic fractionation patterns in oceanic dissolved organic matter: anunaltered photoautotrophic source for dissolved organic nitrogen in theocean? Mar Chem 92:123–134
McClelland JW, Montoya JP (2002) Trophic relationships and the nitrogenisotopic composition of amino acids in plankton. Ecology 83:2173–2180
McMahon KW, Fogel ML, Elsdon TS, Thorrold SR (2010) Carbon isotope
fractionation of amino acids in fish muscle reflects biosynthesis and isotopic
routing from dietary protein. J Anim Ecol 79:1132–1141
McMahon KW, McCarthy MD (2016) Embracing variability in amino acid δ15N
fractionation: mechanisms, implications, and applications for trophic ecology.
Ecosphere 7:e01511
McMahon KW, Thorrold SR, Elsdon TS, McCarthy MD (2015) Trophic
discrimination of nitrogen stable isotopes in amino acids varies with dietquality in a marine fish. Limnol Oceanogr 60:1076–1087
McMahon KW, Thorrold SR, Houghton LA, Berumen ML (2016) Tracing carbon
flow through coral reef food webs using a compound-specific stable isotope
approach. Oecologia. 180:809–821
Melzer E, Schmidt H-L (1987) Carbon isotope effects on the pyruvate
dehydrogenase reaction and their importance for relative carbon-13
depletion in Lipids. J Biol Chem 262:8159–8164
Merritt DA, Hayes JM (1994) Nitrogen isotopic analyses by isotope-ratiomonitoring gas chromatography/mass spectrometry. J Am Soc Mass
Spectrom 5:87–397
Metges CC, Daenzer M (2000) 13C gas chromatography-combustion isotope ratio
mass spectrometry analysis of N-pivaloyl amino acid esters of tissue andplasma samples. Anal Biochem 278:156–164
Metges CC, Petzke K-J, Hennig U (1996) Gas chromatography/combustion/
isotope ratio mass spectrometric comparison of N-acetyl- and N-pivaloyl
amino acid esters to measure 15N isotopic abundances in physiologicalsamples: a pilot study on amino acid synthesis in the upper gastro-intestinal
tract of minipigs. J Mass Spectrom 31:367–376
Monson KD, Hayes JM (1980) Biosynthetic control of the natural abundance ofcarbon 13 at specific positions within fatty acids in Escherichia coli. J Biol
Chem 255:11435–11441
Monson KD, Hayes JM (1982) Carbon isotopic fractionation in the biosynthesis ofbacterial fatty acids. Ozonolysis of unsaturated fatty acids as a means ofdetermining the intramolecular distribution of carbon isotopes. Geochim
Cosmochim Acta 46:139–149
Morra KE, Chikaraishi Y, Gandhi H, James HF, Rossman S, Wiley AE, Raine AF, Beck
J, Ostrom PH (2019) Trophic declines and decadal-scale foraging segregationin three pelagic seabirds. Oecologia 189:395–406
O’Brien DM, Boggs CL, Fogel ML (2005) The amino acids used in reproduction by
butterflies: a comparative study of dietary sources using compound-specificstable isotope analysis. Physiol Biochem Zool 78:819–827
O’Brien DM, Fogel ML, Boggs CL (2002) Renewable and nonrenewable resources:
Amino acid turnover and allocation to reproduction in Lepidoptera. Proceed
Nat Acad Sci 99:4413–4418
Ohkouchi N, Chikaraishi Y, Close HG, Fry B, Larsen T, Madigan DJ, McCarthy MG,
McMahon KW, Nagata T, Naito YI, Ogawa NO, Popp BN, Steffan SA, Takano Y,
Tayasu I, Wyatt ASJ, Yamaguchi YT, Yokoyama Y (2017) Advances in the

Takizawa et al. Progress in Earth and Planetary Science

Takizawa et al. Progress in Earth and Planetary Science(2020) 7:50

application of amino acid nitrogen isotopic analysis in ecological andbiogeochemical studies. Org Geochem 113:150–174
Ohkouchi N, Ogawa NO, Chikaraishi Y, Tanaka H, Wada E (2015) Biochemical andphysiological bases for the use of carbon and nitrogen isotopes inenvironmental and ecological studies. Prog Earth Planetary Sci 2:1
Ostrom PH, Wiley AE, James HF, Rossman S, Walker WA, Zipkin EF, Chikaraishi Y(2017) Broad-scale trophic shift in the pelagic North Pacific revealed by anoceanic seabird. Proc R Soc B 284:20162436
Ostrom PH, Zonneveld J-P, Robbins LL (1994) Organic geochemistry of hardparts: assessment of isotopic variability and indigeneity. Palaeogeogr
Palaeoclimatol Palaeoecol 107:201–212
Popp BN, Graham BS, Olson RJ, Hannides CCS, Lott M, López-Ibarra G, GalvánMagaña F (2007) Insight into the trophic ecology of yellowfin tuna, Thunnus
albacares, from compound-specific nitrogen isotope analysis ofproteinaceous amino acids. In: Dawson TE, Siegwolf RTW (eds) Stable
isotopes as indicators of ecological change. Academic Press, San Diego, pp
173–190
Rieley G (1994) Derivatization of organic compounds prior to gas
chromatographic-combustion-isotope ratio mass spectrometric analysis:
identification of isotope fractionation processes. Analyst 199:915–919
Silfer JA, Engel MH, Macko SA, Jumeau EJ (1991) Stable carbon isotope analysisof amino acid enantiomers by conventional isotope ratio mass spectrometry
and combined gas chromatography/isotope ratio mass spectrometry. Anal
Chem 63:370–374
Smith CI, Fuller BT, Choy K, Richards MP (2009) A three-phase liquid
chromatographic method for δ13C analysis of amino acids from biologicalprotein hydrolysates using liquid chromatography-isotope ratio mass
spectrometry. Anal Biochem 390:165–172
Steffan SA, Chikaraishi Y, Currie CR, Horn H, Gaines-Day HR, Pauli JN, Zalapa JE,
Ohkouchi N (2015) Microbes are trophic analogs of animals. Proc Natl Acad
Sci 112:15119–15124
Steffan SA, Chikaraishi Y, Dharampal PS, Pauli JN, Guédot C, Ohkouchi N (2017)
Unpacking brown food-webs: animal trophic identity reflects rampant
microbivory. Ecol Evol 7:3532–3541
Steffan SA, Chikaraishi Y, Horton DR, Ohkouchi N, Singleton ME, Miliczky E, Hogg
DB, Jones VP (2013) Trophic hierarchies illuminated via amino acid isotopic
analysis. PLOS ONE 8:e76152
Takano Y, Chikaraishi Y, Imachi H, Miyairi Y, Ogawa NO, Kaneko M, Yokoyama Y,
Krüger M, Ohkouchi N (2018) Insight into anaerobic methanotrophy from
13 12
C/ C- amino acids and 14C/12C-ANME cells in seafloor microbial ecology.
Sci Rep 8:14070
Takizawa Y, Chikaraishi Y (2014) Are baby sprouts eating the proteins in themother sweet potato? Res Organic Geochem 30:29–32
Takizawa Y, Chikaraishi Y (2017) Change in the δ15N value of plant aminoacids on the phenology of leaf flush and senescence. Res Organic
Geochem 33:1–6
Takizawa Y, Dharampal PS, Steffan SA, Takano Y, Ohkouchi N, Chikaraishi Y(2017) Intra-trophic isotopic discrimination of 15N/14N for amino acids in
autotrophs: implication for nitrogen dynamics in ecological studies. Ecol
Evol 7:2916–2924
Traugott M, Kamenova S, Ruess L, Seeber J, Plantegenest M (2013) Empiricallycharacterising trophic networks: what emerging DNA based methods, stable
isotope and fatty acid analyses can offer. Adv Ecol Res 49:177–224
Wada E, Ishii R, Aita MN, Ogawa NO, Kohzu A, Hyodo F, Yamada Y (2013) Possible
ideas on carbon and nitrogen trophic fractionation of food chains: a new
aspect of food-chain stable isotope analysis in Lake Biwa, Lake Baikal, andthe Mongolian grasslands. Ecol Res 28:173–181
Watanabe M, Owada M, Kanazawa K (2009) Dietary habits of doridacean sea
slugs of the Sagami Bay. Sci J Kanagawa Univ 20:85–88

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