1

Prediction of metabolic pathways to produce naringenin from the

organisms involved in the fermentation of cocoa and synthesis of value-

added compounds

Lina Suárez Medina1, Andrés González Barrios2

Master's Degree in Chemical Engineering, Chemical Engineering Department, Universidad de los Andes 1.lj.suarez@uninades.edu.co, 2andgonza@uniandes.edu.co

2019-05-30

___________________________________________________________________________________________________________

Abstract

Due to the growing interest in beneficial health products, such as functional foods, dietary supplements, and nutraceuticals, various

techniques and methods have been developed to obtain products with nutritional and pharmaceutical components that contribute to

consumer health. It is known that cocoa beans contain high levels of polyphenols, which are mostly present in the form of flavonoids,

recognized for their antioxidant capacity and health benefits. The fermentation of cocoa is a critical process in the synthesis of the

organoleptic and nutritional characteristics of chocolate, being derived from polyphenols such as flavonoids. Based on this, the interest

in knowing the metabolic pathways for the generation of flavonoids and added value novel products from fermentation, aiming to give

an added value to the derivatives of chocolate and why not, to the design of new products with high health benefits. In order to predict

a set of alternative metabolic pathways to produce the flavonoid precursor naringenin from the amino acids L-tyrosine and L-

phenylalanine, the OptStoic algorithm was performed. Two metabolic pathways were obtained for naringenin flavonoid production,

one per amino acid, encoded by the genes PAL / TAL, 4CL, CHI, CHS from L-tyrosine and the C4H gene additional to the previous

ones from L-phenylalanine. The FBA was performed to analyze the metabolic capacities of E. coli to produce naringenin by

incorporating the non-native path predicted using the strain E. coli ATCC 8739, obtaining a growth of 0.8616 mmol/gDW-1*h-1 and flux

of naringenin production of 0.1231 mmol/gDW-1*h-1 Finally, an OptKnock analysis was carried out with which the set of knockout

reactions was obtained to improve flavonoid production composed of CITL and MALS, generating a biomass growth of 0.887

mmol/gDW-1*h-1 and a naringenin production coupled to the growth of 0.1267 mmol/gDW-1*h-1. Since naringenin is a commercially

available compound, the chemical synthesis of its apiforol derivative was carried out and its antioxidant activity was checked through

the FRAP method.

Keywords: Metabolic pathways, nutraceutical, cocoa fermentation, algorithm, naringenin, apiforol. _________________________________________________________________________________________________________________________

Introduction Theobroma cacao is a plant native from South

America1 From this plant, the raw material for the

production of chocolate is obtained, which is composed

of the seeds inside the cocoa fruits2. A crucial process for

the correct formation of the precursors of cocoa flavor,

amino acids, and the short-chain peptides, which favor

the biochemical changes within the grains, is

fermentation2–4. The overall result of this process has a

profound impact on the color and flavor potential of the

beans5, being key in the determines final cocoa quality6.

Currently, the offer of added valued products

focuses on improving human health, has encouraged the

food industry to research and generate beneficial foods,

exalting their properties nutritional and pharmaceutical,

as is the case of functional foods, dietetic supplements,

and nutraceutical7. Among the nutraceuticals are

antioxidants such as vitamins C and E, phenolic

compounds such as flavonoids, saponins, fatty acids,

phospholipids in others8. From this point of view, cocoa

is very important, due to its high content of polyphenols,

which are related to the organoleptic characteristics and

the nutritional value of the final product. Previously,

high amounts of flavonoids have been found in cocoa,

mainly (−)-epicatechin (EC), (+)–catechin (C), and their

dimers procyanidin B2 (PB2) and B1 (PB1), which have

been reported effects on many important biological

functions in vitro and in vivo, due to their antioxidant,

anti-free radical and antiplatelet impacts. Furthermore,

they are able to modulate the immune response, anti-

inflammatory, anti-diabetic and anti-carcinogenic

properties9.

An interesting compound found in the

Theobroma cacao genome reported in the KEGG

database is the naringenin flavonoid. Naringenin has

been reported as a natural compound that has many

effects on human health, as agent normolipidemic,

inhibitor and activator of important enzymes in the

oxidation and synthesis fatty acids (Goldwasser, Cohen,

Yang, Balaguer, Yarmush, 2010). It also has been

shown effects in decreasing Hepatitis C virus (HCV) in

sick rats11. This has antioxidant, anti-inflammatory,

antiproliferative and antimutagenic properties, and it is

suggested as an active chemoprotector12, it fights liver,

coronary and obesity diseases and serves as a treatment

for the dengue virus 13. Naringenin is commercially

available in several forms, as a nutritional supplement,

however, the compound derived from this, flavan-4-ol

2

apiforol, obtained by the action of the enzyme flavanone

4-reductase, is a compound that is currently not

commercialized and which, in turn, it has been reported

as beneficial for health. This compound can be obtained

through a reduction reaction with sodium borohydride,

where the ketone group from naringenin is reduced to

alcohol group forming apiforol. Also, apiforol showed a

significant α-glucosidase inhibitory activity so that it can

be considered as a potent antidiabetic agent as it

exhibited a reduction in the postprandial hyperglycemia,

by inhibiting the digestive enzymes and enhancing the

uptake of glucose by the cells14. For this reason, apiforol

is a promising and high impact compound.

With advances in DNA sequencing and

recombinant DNA technology, many biosynthetic

pathways responsible to produce valuable compounds

have been elucidated, offering the opportunity for

functional integration of biosynthetic pathways into

suitable microorganisms, such as Escherichia coli or

Saccharomyces cerevisiae. Through metabolic

engineering, we can modify the metabolic profiles

according to the purposes of individual production and

heterologous expression of the complete pathways of

plants to confer the ability to synthesize natural products

derived from plants, as is the case of naringenin and the

apiforol15. The central structure of the flavonoids was

derived directly from the amino acids L-phenylalanine

and L-tyrosine, through the elimination of a non-

oxidative amino group by a phenylalanine amino-lyase

(PAL) or a tyrosine-amino-lyase (TAL) respectively,

generating access to various flavonoid precursors15. This

is the case of the flavonoid precursor, naringenin.

Recently, an economic process has been

developed without the use of precursor amino acids for

the microbial production of naringenin from glucose16.

This was achieved through the introduction of a

heterologous pathway, consisting of chalcone synthase

(CHS), tyrosine-amino-lyase (TAL) maximized by

codons, 4-coumaroyl-coa (4CL) and chalcone

isomerase (CHI), in E. Coli strains designed to produce

L-tyrosine. These strains were able to produce 29 mg/L

of naringenin from glucose and 84 mg/l of naringenin

from glucose when the enzymes involved in fatty acid

biosynthesis were further suppressed.

Based on technological advances and the need to

predict and design new metabolic pathways, in order to

obtain new useful products for industry and human

development, several studies of design and development

of bioinformatics tools have been developed with the

help of the systems biology, with the aim of creating

algorithms capable of predicting metabolic pathways

based on their topology (length analysis), the metabolic

efficiency of the host organism, energy costs, genetic

loads17, COnstraint-Based Reconstruction and Analysis

(COBRA)18, making it possible to approach the design

of pathways from the quantitative and functional point

of view, obtaining results of cellular metabolisms that

have successfully predicted the production of several

compounds18. In addition, the use of synthetic tools from

systems biology allowed the regulation of genes, the

assembly of heterologous pathways and the temporal

control of gene expression.

At present, several in silico mathematical

methods have been constructed to design and predict

synthetic metabolic pathways, such as GEN-Path

(Genome-Scale-Model Predictor Pathway)18 and

FindPath17. These use Flux Balance Analysis (FBA),

steady-state, databases with information on compounds,

objective functions of biomass growth and

thermodynamic restrictions. On the other hand, OptStoic

is a conversion algorithm for overall stoichiometry and

intermediate metabolic reactions, which is based on two

steps: 1, identification of optimal general stoichiometry

of a general reaction formulated for prediction, 2,

identification of the reaction pathways that conform to

the overall stoichiometry identified in the first step19.

In this work we want to obtain de novo metabolic

pathways, enzymes and associated genes, in the process

of fermentation of cocoa seeds, for the production of the

naringenin flavonoid precursor from the amino acids L-

phenylalanine and L-tyrosine and synthesize the apiforol

compound from the naringenin.

1. Methods and materials

Obtaining metabolic pathways for naringenin

production. OptStioc was used to identify optimal

overall stoichiometry and MinFlux was used to predict

metabolic pathways adjusted to overall stoichiometry

identified by Optstoic. OptSotic formulation is a MILP

(Mixed Integer Linear Programming) or IP (Integer

Programming Problem) problem, depending on whether

the stoichiometric coefficients are declared as real or

integer variables, respectively. This formulation allows

identifies the overall stoichiometry of conversion from a

reagent A (in this case L-tyrosine and L-phenylalanine)

to a product B (naringenin), maximizing a desired yield

objective subject to overall constraints of elemental and

charge balance and thermodynamic feasibility, allowing

combinations between cofactors (co-reactive D and co-

product C), from a metabolite database and their

respective stoichiometric coefficients in the general

conversion (b, c, d).

Overall conversion equation:

𝒂𝑨 + 𝒄𝑪 → 𝒃𝑩 + 𝒅𝑫 (1)

3

Where A, B, C, and D, are the metabolites of the

reaction and a, b, c, and d, are the stoichiometric

coefficients of the metabolites, which must be positive

and not null.

The overall optimization formulation is:

𝑴𝒂𝒙𝒊𝒎𝒊𝒛𝒆 𝒁𝒑 = ∑𝑪𝒊

−𝑪𝑨𝒊∈𝑷𝑫𝑻

(2)

Subject to:

𝒏𝑩𝒒𝒃 + 𝒏𝑫𝒒𝒅 − 𝒏𝑨𝒒𝒂 − 𝒏𝑪𝒒𝒄 = 𝟎, ∀ 𝐪 ∈ 𝐐 (3)

𝒆𝑩𝒃 + 𝒆𝑫𝒅 + 𝒆𝑪𝒅 + 𝒆𝑪𝒄 = 𝟎, (4)

∆𝑮𝑩𝒇

𝒃 + ∆𝑮𝑫𝒇

𝒅 − ∆𝑮𝑪𝒇

𝒄 − ∆𝑮𝑨𝒇

𝒂 ≤ −∆𝑮𝒎𝒊𝒏 (5)

𝒉(𝒂, 𝒃, 𝒄, 𝒅) = 𝟏 (6)

𝒂, 𝒃, 𝒄, 𝒅 ∈ 𝑹+/𝒁 (7)

Where i = 1... N in the set I of metabolites present

in the database and q ϵ Q is the set of elements

found in the metabolites, 𝑛𝑖𝑞 is the number of atoms of

element q in metabolite i, 𝑒𝑖 is the charge of metabolite

i, ∆𝐺𝑖𝑓

is the transformed free energy of formation (at 25

°C and 0.1 M ionic strength) of metabolite i, ∆𝐺𝑔𝑒𝑛𝑒𝑟𝑎𝑙

must be less to zero and y ∆𝐺𝑚𝑖𝑛 is the extra energy

change amount available for the stoichiometry and h is

stoichiometry of the base metabolite, which is set at a

value of 1. 𝑍𝑝 is the yield with respect to the reagent,

𝑃𝐷𝑇 is a subset of target products and 𝐶𝑖 is the

stoichiometric coefficient of metabolite i.

To run OptStoic, two metabolites databases were

constructed, one with the metabolites related with

metabolic reactions of the organisms of cocoa seeds

fermentation and other with the metabolites belong to

KEGG database, which includes net charges and

reversibility (∆𝐺𝑖𝑓) for each metabolite from Pactric20

and Metacyc21, CheBi22 and PubChem23 databases. Once

the stoichiometry and the cofactors involved in the

overall reaction have been obtained, the metabolic

pathways to contain these metabolites are identified by

minimizing the total metabolic flux through minFlux19

formulation.

Set of minimum reactions that can match the

desired overall stoichiometry is formulated with the

following linear programming problem (LP), which

minimizes the total metabolic flux of the chosen

reactions. The objective of this optimization problem is

to minimize the reaction flux, which approximates the

total enzyme load imposed on the organism. This

problem can be described mathematically as:

𝑴𝒊𝒏𝒊𝒎𝒊𝒛𝒆 (𝒙𝒋) ∑ 𝒙𝒋

𝒋 ∈ 𝑱

(𝒎𝒊𝒏𝑭𝒍𝒖𝒙) (8)

Subject to:

∑ 𝑺𝒊𝒋𝒗𝒋

𝒋 ∈ 𝑱

= 𝟎, ∀ 𝒊 ∈ 𝑰 (9)

𝒗𝑨𝒆𝒙 = −𝒂, 𝒗𝑪

𝒆𝒙 = −𝒄, 𝒗𝑩𝒆𝒙 = −𝒃, 𝒗𝑫

𝒆𝒙

= −𝒅 (10)

𝑳𝑩𝒋 ≤ 𝒗𝒋 ≤ 𝑼𝑩𝒋 , ∀ 𝒋 ∈ 𝑱 (11)

𝒙𝒋 ≥ 𝒗𝒋, ∀ 𝒋 ∈ 𝑱 (12)

𝒙𝒋 ≥ −𝒗𝒋, ∀ 𝒋 ∈ 𝑱 (13)

𝑣𝑗 , 𝑥𝑗 ∈ 𝑅

𝜺𝒖𝒍 ≤ 𝑺𝒍 ≤ 𝑴𝒖𝒍, ∀ 𝒍 ∈ 𝑳 (14)

∑ (𝟏 − 𝒖𝒍)

𝒍|𝒖𝒍𝒌=𝟏

≥ 𝟏, ∀ 𝒌 = 𝟏, … , 𝑲 (15)

𝒗𝒋 ≥ 𝑳𝑩𝒋𝒖𝒋, ∀ 𝒋|∆𝑮𝒋𝟎 ≤ 𝟎 (161)

𝒗𝒋 ≤ 𝑼𝑩𝒋𝒖𝒋, ∀ 𝒋|∆𝑮𝒋𝟎 ≥ 𝟎 (17)

∑ 𝒖𝒋

𝒋

≤ 𝒑𝒕 (18)

𝒖𝒋 = {𝟏, 𝒊𝒇 𝒔𝒊𝒈𝒏(𝒗𝒋) = 𝒔𝒊𝒈𝒏(∆𝑮𝒋

𝟎)

𝟎, 𝒐𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆 (19)

Where 𝑖, 𝑗 are metabolites and reactions,

respectively, 𝑣 is the flux dual variable of each reaction

(Include inside and exchange reactions), 𝑆𝑖𝑗:

Stoichiometry matrix that describes the coefficient of

metabolite i in reaction j, 𝑣𝐴𝑒𝑥, 𝑣𝐶

𝑒𝑥, 𝑣𝐵𝑒𝑥, 𝑣𝐷

𝑒𝑥 are the

exchange fluxes of the participating metabolites in the

global reaction, which are equal to their stoichiometric

coefficients, 𝐿𝐵𝑗 𝑎𝑛𝑑 𝑈𝐵𝑗 are lower and upper bounds

on the flux of each reaction j, 𝑥𝑖 is the sum of the absolute

values of the reaction fluxes, 𝑣𝑗 is reaction fluxes vector,

𝜀 is a small positive value (𝜀 = 10−5) that ensures that

𝑆𝑙 is nonzero when 𝑢𝑙 = 1. Binary variables 𝑢𝑙 denote

the presence of nonzero basic variable l in a previously

identified solution k, ∆𝐺𝑗0

is the free energy change of the

reaction under standard conditions, 𝑝𝑡 is a parameter that

keeps the total number of reactions with a positive

standard change in free energy to a minimum and 𝑢𝑗 is a

binary variable that selects the flows of the reactions.

To run minFlux, two inputs sets were

constructed, the first with the metabolic information of

the species reported in the literature (see Table 1¡Error!

No se encuentra el origen de la referencia.) for the

4

fermentation process of cocoa seeds, including the

information Theobroma cacao plant. In these inputs,

species without a metabolic model were not used.

Additionally, the reactions involved in glycolysis, the

pentose phosphate pathway, the phenylalanine, tyrosine,

and tryptophan biosynthesis and the secondary

metabolism of the Theobroma cacao plant were

considered. Moreover, the second inputs all the

metabolic information of the KEGG database (Kyoto

Encyclopedia of Genes and Genomes)24–26 were

included.

Integration of metabolic pathways predicted

on an E. coli in silico model. Once the predicted

pathways were obtained, these were mapped using the

Escher web tool27 and a Flux Balance Analysis (FBA)

was carried out. FBA was performed to analyze the

metabolic capabilities of E. coli to produce naringenin

by incorporation of nonnative pathways predicting

through OptStoic. This algorithm was used following the

mathematical formulation reported by Savinell and

Palsson 199228. The most attractive feature of this tool is

its ability to make quantitative predictions in a network,

without the need for detailed kinetic information for its

development, only the stoichiometry of the reactions, a

defined objective biological function (production of

biomass, synthesis of metabolites, redox potential, etc.)

and growth conditions (availability of substrates)29. For

FBA, the model of the organism E. coli ATCC 8739 was

used and the execution was performed under aerobic

conditions and with a glucose uptake of -10 mmol*gDW-

1*h-1 as a carbon source. FBA can be described as follow:

𝒎𝒂𝒙𝒊𝒎𝒊𝒛𝒆 𝒁 = 𝑪𝑻𝒗𝒋 (20)

Subject to:

𝒅𝑪

𝒅𝒕= 𝑺𝒊,𝒋 ∙ 𝒗𝒋 = 𝟎 (21)

𝑳𝑩𝒋 ≤ 𝒗𝒋 ≤ 𝑼𝑩𝒋 (22)

Where 𝐶𝑇is transposed vector of the

concentrations for each species.

Improving the naringenin production in E.

coli through OptKnock analysis. Due the fact that

cellular metabolism is prepared, through natural

selection, for the maximum responsiveness to the

history of selective pressures rather than for the

overproduction of specific chemical compounds, so that

the yield of these products on several organisms are

often far below their theoretical maximums, as is the

case of production of plants products in bacterial, for

example, naringenin production. With the aim of

improving the reaction flux for naringenin production in

E. coli, an OptKnock30 analysis was carried out in

COBRA toolbox. OptKnock suggests gene knockout

strategies for biochemical overproduction while

recognizing that metabolic flux distributions are

governed by internal cellular objectives30. This tool

identifies and eliminates metabolic reactions that are

capable of decoupling cell growth from chemical

production. Involving optimization at two levels, which

considers two optimal strategists competing with each

other (i.e. the cell target and chemical production) by

identifying multiple combinations of gene deletion. In

this case, biomass formation was used as a cell target and

naringenin production as chemical production.

OptKnock can be described as:

𝒎𝒂𝒙𝒊𝒎𝒊𝒛𝒆(𝒚𝒊) 𝒗𝒄𝒉𝒆𝒎𝒊𝒄𝒂𝒍 (23)

Subject to:

𝒗𝒃𝒊𝒐𝒎𝒂𝒔𝒔 = 𝒗𝒂𝒕𝒑−𝒎𝒂𝒊𝒏 . 𝝁𝒂𝒕𝒑 + 𝒗𝒃𝒊𝒐𝒎𝒂𝒔𝒔𝒐𝒃𝒋

. 𝝁𝒃𝒊𝒐𝒎𝒂𝒔𝒔

+ 𝒗𝒈𝒍𝒄−𝒖𝒑𝒕𝒂𝒌𝒆. 𝒈𝒍𝒄 (24)

Subject to:

∑ 𝑺𝒊𝒋𝒗𝒋 = 𝟎

𝑴

𝒋=𝟏

∀𝒊 ∈ 𝑵 (25)

𝒗𝒑𝒕𝒔 + 𝒗𝒈𝒍𝒌 = 𝒗𝒈𝒍𝒄−𝒖𝒑𝒕𝒂𝒌𝒆 (26)

𝒗𝒂𝒕𝒑 ≥ 𝒗𝒂𝒕𝒑−𝒎𝒂𝒊𝒏 (27)

∑ 𝜸𝒊𝒔𝒕𝒐𝒊𝑺𝒊,𝒈𝒍𝒌

𝑵

𝒊=𝟏

+ 𝒖𝒈𝒍𝒌 + 𝒈𝒍𝒄 = 𝟎 (28)

∑ 𝜸𝒊𝒔𝒕𝒐𝒊𝑺𝒊,𝒑𝒕𝒔

𝑵

𝒊=𝟏

+ 𝒖𝒑𝒕𝒔 + 𝒈𝒍𝒄 = 𝟎 (29)

∑ 𝜸𝒊𝒔𝒕𝒐𝒊𝑺𝒊,𝒃𝒊𝒐𝒎𝒂𝒔𝒔

𝑵

𝒊=𝟏

+ 𝒖𝒃𝒊𝒐𝒎𝒂𝒔𝒔 = 𝟏 (30)

∑ 𝜸𝒊𝒔𝒕𝒐𝒊𝑺𝒊,𝒋

𝑵

𝒊=𝟏

+ 𝒖𝒋 = 𝟎, ∀𝒋 ∈ 𝝁, 𝒋 ≠ 𝒈𝒌𝒍, 𝒑𝒕𝒔, 𝒃𝒊𝒐𝒎𝒂𝒔𝒔 (31)

∑(𝟏 − 𝒚𝒋)

𝒋∈𝑴

≤ 𝑲 (32)

𝒗𝒃𝒊𝒐𝒎𝒂𝒔𝒔 ≥ 𝒗𝒃𝒊𝒐𝒎𝒂𝒔𝒔𝒐𝒃𝒋

(33)

𝒖𝒋𝒎𝒊𝒏. (𝟏 − 𝒚𝒋) ≤ 𝒖𝒋 ≤ 𝒖𝒋

𝒎𝒂𝒙. (𝟏 − 𝒚𝒋), ∀𝒋 ∈ 𝝁𝒓𝒆𝒗 𝒚 𝒋

∉ 𝝁𝒔𝒆𝒄𝒓 (34)

𝒖𝒋𝒎𝒊𝒏. (𝟏 − 𝒚𝒋) ≤ 𝒖𝒋, ∀𝒋 ∈ 𝝁𝒓𝒆𝒗 𝒚 𝝁𝒔𝒆𝒄𝒓 (35)

𝒖𝒋 ≤ 𝒖𝒋𝒎𝒂𝒙. (𝟏 − 𝒚𝒋), ∀𝒋 ∈ 𝝁𝒊𝒓𝒓𝒆𝒗 𝒚 𝒋 ∉ 𝝁𝒔𝒆𝒄𝒓 (36)

𝒖𝒋 ∈ 𝑹, ∀𝒋 ∈ 𝝁𝒊𝒓𝒓𝒆𝒗 𝒚 𝝁𝒔𝒆𝒄𝒓 (37)

𝒗𝒋𝒎𝒊𝒏. 𝒚𝒋 ≤ 𝒗𝒋 ≤ 𝒗𝒋

𝒎𝒂𝒙. 𝒚𝒋, ∀ 𝒋 ∈ 𝝁 (38)

5

𝒚𝒋 = {𝟎, 𝟏}, ∀ 𝒋 ∈ 𝝁 (39)

𝜸𝒋 = {𝟎, 𝟏}, ∀𝒋 ∈ 𝑵 (40)

Where 𝑣𝑔𝑙𝑐−𝑢𝑝𝑡𝑎𝑘𝑒 is glucose basic uptake,

𝑣𝑎𝑡𝑝−𝑚𝑎𝑖𝑛 is the maintenance requirement of ATP not

associated with growth, 𝑣𝑏𝑖𝑜𝑚𝑎𝑠𝑠𝑜𝑏𝑗

is a minimal level of

biomass production, 𝑣𝑝𝑡𝑠 is glucose uptake through the

phosphotransferase system, 𝑣𝑔𝑙𝑘 is glucose uptake

through the glucokinase system, 𝑦 is a binary variable (1

if the flux of the reaction is active or 0 otherwise), 𝜇 is

the transport reaction set (reversible (𝑣𝑟𝑒𝑣) and

irreversible (𝑣𝑖𝑟𝑟𝑒𝑣) of metabolites only secreted (𝑣𝑠𝑒𝑐𝑟),

𝑁 is the metabolites set, 𝐾 is the number of knockouts

allowed, 𝛾 is a dual variable associated with the

stoichiometric constraints, 𝑔𝑙𝑐 is a dual variable dual

associate with the glucose uptake, 𝑢 is a dual variable

associated with any other constraints in its

corresponding flux.

Possibles added-value compounds from

naringenin. Because naringenin is a product with

several commercial presentations as a nutritional

supplement, it is necessary to investigate novel products

for the pharmaceutical and food industry. Based on this,

two criteria were used to select those products derived

from naringenin 1. The chemical compound must be

beneficial for human health and 2. This, in turn, may not

have commercial products available.

Obtaining apiforol as a novel product and

measuring its antioxidant capacity. Apiforol is a

derivative of naringenin, which has been reported with

antidiabetic properties14. In order to obtain apiforol from

naringenin, a chemical synthesis was performed.

Naringenin (20 mg) was dissolved in 400 µl of 100%

ethanol and reduced with sodium borohydride (10 mg).

After 45 minutes, the reduction product was acidified

with 10% (v/v) acetic acid (200 µl) in ethanol (1800 µl)

to turn off the borohydride and reduce the pH of the

solution. Later, this was evaporated at a temperature of

35 ° C and the residue containing apiforol was stored at

room temperature in the dark. The presence of flavan-4-

ol was confirmed by the formation of a red pigment in

the presence of 30% (v/v) hydrochloric acid in ethanol31.

With the aim to check if indeed apiforol had health

benefices an antioxidant capacity assay was carried out.

Antioxidant capacity of apiforol was measured through

FRAP (ferric reducing/antioxidant power) method,

which is based on an increase in the absorbance at 593

nm due to formation of TPTZ-Fe(II)32. This method

evaluates the antioxidant capacity of a sample according

to its ability to reduce ferric iron Fe(III) present in a

complex with 2,4,6-tri (2-pyridyl) -s-triazine (TPTZ) up

to the ferrous form Fe(II)32. The reaction mixture FRAP

was prepared by mixing 25 mL of 0.3 mol/L sodium

acetate buffer solution (pH 3.6), 2.5 mL of 10 mmol/L

TPTZ and 2.5 mL of 20 mmol/L FeCl3. Reaction mixture

was incubated at 37°C for 30 min. Different

concentrations of apiforol (0.1, 0.2, 0.3 mg/mL) were

mixed with reaction mixture, as follows: 3 ml prepared

FRAP reagent, 100 µL of sample was then added, along

with 300 µl H2O. Absorbance readings were taken after

0.5 s and every 15 s thereafter during the monitoring

period at 593 nm. This assay was carried out in the T75

UV-VIS Spectrophotometer

2. Results

On the consulted literature, 71 species of

organisms were found (See Table 1¡Error! No se

encuentra el origen de la referencia.), 15 (21.1%)

yeasts (Y), 6 (8.5%) acidic acid bacteria (AAB), 22

(30.9%) lactic acid bacteria (LAB) and 28 (39.5%)

organisms not classified in the three previous categories

and in which the Enterobacteriaceae are found, such as

Erwinia and Klebsiella2. Also, families of infectious

viruses for the bacteria Myoviridae and Siphoviridae are

reported33. From these organisms, only 44 organisms

were found with metabolic models in specialized

databases such as Metacyc and Patric (information

associated with EC Number see Table 1. On average, the

species have about 1066 reactions and 1164 metabolites.

OptStoic formulation of the overall reaction.

OptStoic was run with the information from the KEGG

database, a total of 10,195 reactions and 18,120

metabolites were obtained. The Gibbs free energy

change (ΔGf) and the net charge of each metabolite were

extracted from the Metacyc and chEBI databases.

Because OptStoic generates the stoichiometric

coefficients that satisfy the optimum stoichiometry for

the general reaction, two global reactions were proposed

from two flavonoid precursors, with the aim of finding

which of these reactions satisfies the thermodynamic

constraints while this is feasible for production of the

target product. To produce the naringenin flavonoid

precursor, two reactants were chosen, the amino acids L-

phenylalanine and L-tyrosine.

Naringenin production from L-phenylalanine.

𝑳_𝒑𝒉𝒆𝒏𝒚𝒍𝒂𝒍𝒂𝒏𝒊𝒏𝒆 → 𝑵𝒂𝒓𝒊𝒏𝒈𝒆𝒏𝒊𝒏 (41)

Naringenin production from L-tyrosine.

𝑳_𝒕𝒚𝒓𝒐𝒔𝒊𝒏𝒆 → 𝑵𝒂𝒓𝒊𝒏𝒈𝒆𝒏𝒊𝒏 (42)

To make these reactions chemically viable, the

compounds that make up the LB growth medium were

added to the algorithm (See Table 2).

6

Table 1. Microorganisms reported in the literature for the fermentation of cocoa seeds in samples from Cameroon, Ghana, Cote d'Ivoire, Brazil, Malaysia, India, Mexico, and Cuba.

Organisms Metabolic model Source Ref. Organisms Metabolic model Source Ref.

Yeasts (Y) Pediococcus acidilactici Pediococcus acidilactici MA18/5M Metacyc 40

Hanseniaspora uvarum --- 35, 36, 34, 37 Lactobacillus pentosus Lactobacillus pentosus KCA1 Metacyc 38

Hanseniaspora opuntiae --- 34, 38, 39 Leuconostoc

pseudomesenteroides Leuconostoc pseudomesenteroides 4882 Metacyc 38

Hanseniaspora guilliermondii --- 35, 36 Leuconostoc mesenteroides Leuconostoc mesenteroides cremoris ATCC

8293 Metacyc 34

Saccharomyces cerevisiae Saccharomyces cerevisiae S288c Metacyc 40, 38, 41, 36, 37, 34, 42, 39 Lactococcus lactis Lactococcus lactis lactis IL1403 Metacyc 34

Saccharomycodes ludwigii --- 43 Enterococcus camelliae --- 2

Candida magnolia --- 40 Weissella paramesenteroides Weissella paramesenteroides ATCC 33313 Metacyc 38, 2, 46, 47, 48

Candida carpophila --- 39 Enterococcus casseliflavus Enterococcus casseliflavus ATCC 12755 Metacyc 2, 47 Candida orthopsilosis --- 39 Others

kudriavzevii (Issatchenkia orientalis) --- 39, 36 Zymomonas mobilis Zymomonas mobilis mobilis ATCC 10988 Metacyc 34

Pichia manshurica --- 39 Staphylococcus saprophyticus Staphylococcus saprophyticus saprophyticus

ATCC 15305 Metacyc 37, 2, 42

Meyerozyma caribbica --- 39 Paenibacillus lautus --- 40

Yamadazyma mexicana --- 39 Paenibacillus illinoisensis --- 40

Pichia kluyveri --- 36, 37 Paenibacillus chibensis --- 40

Kodamaea ohmeri --- 39 Oenococcus oeni Oenococcus oeni ATCC BAA-1163 Metacyc 34

Lachancea thermotolerans --- 34, 43 Lysinobacillus boronitolerans --- 2

Acetic acid bacteria (AAB) Lysinibacillus sphaericus Lysinibacillus sphaericus C3-41 Metacyc 40

Acetobacter pasteurianus Acetobacter pasteurianus 386B Metacyc 34, 35, 38, 2, 40, 44 Lysinibacillus fusiformis Lysinibacillus fusiformis Metacyc 40

Acetobacter syzygii --- 2 Klebsiella pneumoniae Klebsiella pneumoniae KCTC 2242 Metacyc 2, 49

Acetobacter senegalensis Acetobacter senegalensis 108B Metacyc 38, 2, 44 Erwinia tasmaniensis Erwinia tasmaniensis Et1/99 Metacyc 34

Acetobacter sicerae --- 2 Corynebacterium variabile Corynebacterium variabile DSM 44702 Metacyc 2

Acetobacter tropicalis Acetobacter tropicalis NBRC 101654 Metacyc 37, 2, 44, 42 Bacillus subtilis Bacillus subtilis168 Metacyc 37, 34, 42, 50, 51

Gluconobacter Oxidans --- 40 Bacillus safensis --- 2

Lactic acid bacteria (LAB) Bacillus altitudinis --- 40

Lactobacillus fermentum Lactobacillus fermentum ATCC 14931 Metacyc 34, 36, 37, 38, 2, 40, 45 Arthrobacter solii --- 2

Lactobacillus brevis Lactobacillus brevis ATCC 367 Metacyc 34 Acinetobacter radioresistens Acinetobacter radioresistens SK82 Metacyc 2

Lactobacillus rhamnosus Lactobacillus rhamnosus ATCC 21052 Metacyc 36, 34, 35 Acinetobacter guillouiae Acinetobacter guillouiae CIP 63.46 Patric 2

Lactobacillus plantarum

Lactobacillus plantarum plantarum

ATCC 14917 = JCM 1149 = CGMCC

1.2437

Metacyc 34, 35, 37, 38, 2, 40, 42, 45, 41 Fructobacillus pseudoficulneus Fructobacillus pseudoficulneus DSM 15468 Metacyc 34

Lactobacillus plantarum /

paraplantarum Lactobacillus paraplantarum L-ZS9 Metacyc 2 Bacillus pumilus18 Bacillus pumilus ATCC 7061 Metacyc 34, 52

Lactobacillus casei Lactobacillus casei LOCK919 Metacyc 36, 34, 35 Bacillus pseudomycoides Bacillus pseudomycoides DSM 12442 Patric 40

Lactobacillus nagelii Lactobacillus nagelii DSM 13675 Metacyc 2 Bacillus patagoniensis --- 40

Lactobacillus vaginalis Lactobacillus vaginalis DSM 5837 =

ATCC 49540 Metacyc 2 Bacillus mojavensis Bacillus mojavensis RO-H-1 = KCTC 3706 Metacyc 40

Lactobacillus ghanensis Lactobacillus ghanensis DSM 18630 Metacyc 2 Bacillus megaterium Bacillus megaterium QM B1551 Patric 37, 34, 42, 51

Lactobacillus coryniformis Lactobacillus coryniformis coryniformis

KCTC 3167 = DSM 20001 Metacyc 40 Bacillus circulans Bacillus circulans NBRC 13626 Patric 2

Lactobacillus curvatus --- 40 Bacillus cereus Bacillus cereus ATCC 10876 Metacyc 34, 50

Lactobacillus mali Lactobacillus mali KCTC 3596 = DSM

20444 Metacyc 40 Bacillus clausii Bacillus clausii KSM-K16 Metacyc 2

Lactobacillus sakei Lactobacillus sakei sakei 23K Metacyc 40 Bacillus amyloliquefaciens Bacillus amyloliquefaciens DSM 7 Metacyc 51

Lactobacillus hilgardii Lactobacillus hilgardii DSM 20176 =

ATCC 8290 Metacyc 40

7

Identify optimal overall stoichiometry. The stoichiometric coefficients obtained for the reactions are the

following:

Naringenin production from L-tyrosine:

𝟏𝟐 𝒕𝒚𝒓𝒐𝒔𝒊𝒏𝒆 + 𝟏𝟒 𝑶𝟐 → 𝟓 𝒏𝒂𝒓𝒊𝒏𝒈𝒆𝒏𝒊𝒏𝒆 + 𝟑 𝒂𝒄𝒆𝒕𝒂𝒕𝒆 + 𝟑 𝒂𝒓𝒈𝒊𝒏𝒊𝒏𝒆 + 𝟗 𝑯𝟐𝑶 + 𝟗 𝑪𝑶𝟐 (43)

𝟏𝟐 𝑪𝟗𝑯𝟏𝟏𝑯𝑶𝟑 + 𝟏𝟒 𝑶𝟐 → 𝟓 𝑪𝟏𝟓𝑯𝟏𝟐𝑶𝟓 + 𝟑 𝑪𝟐𝑯𝟒𝑶𝟐 + 𝟑 𝑪𝟔𝑯𝟏𝟒𝑵𝟒𝑶𝟐 + 𝟗 𝑯𝟐𝑶 + 𝟗 𝑪𝑶𝟐 (44)

Naringenin production from L-phenylalanine:

𝟏𝟐 𝒑𝒉𝒆𝒏𝒚𝒍𝒂𝒍𝒂𝒏𝒊𝒏𝒆 + 𝟗 𝑶𝟐 + 𝟔 𝑪𝑶𝟐 → 𝟔 𝒏𝒂𝒓𝒊𝒏𝒈𝒆𝒏𝒊𝒏𝒆 + 𝟏𝟐 𝒈𝒍𝒚𝒄𝒊𝒏𝒆 (45)

𝟏𝟐 𝑪𝟗𝑯𝟏𝟏𝑵𝑶𝟐 + 𝟗 𝑶𝟐 + 𝟔 𝑪𝑶𝟐 → 𝟔 𝑪𝟏𝟓𝑯𝟏𝟐𝑶𝟓 + 𝟏𝟐 𝑪𝟐𝑯𝟓𝑵𝑶𝟐 (46)

These results were obtained for the input with

total KEGG information, while for the input with

information from organisms of the fermentation process

only the production of naringenin from tyrosine was

obtained. From the above equations, it can be observed

that in the production of naringenin from phenylalanine

and tyrosine, is necessary the participation of arginine

and glycine, respectively. Allowing the stoichiometric

balance of these reactions. Also, the production of

naringenin from tyrosine had an in silico yield of 41%,

which means that to produce 1 molecule of naringenin

2.4 molecules of tyrosine are necessary, and a ∆𝑟𝐺 of -

113.69 kcal. The production of naringenin from

phenylalanine had an in silico yield of 50%, which

means that to produce 1 molecule of naringenin 2

molecules of phenylalanine are necessary, and a ∆𝑟𝐺 of

-50.4 kcal.

Two pathways to naringenin produce from

phenylalanine and tyrosine were predicted. In this

step, the metabolites glycans, polysaccharides composed

of monosaccharides of a single type34, were not taken

into account, so the number of reactions was reduced to

10,195 from 10,668 for the KEGG database and for the

organisms database the reduction was 47,959 reactions

to 39,990. Figure 1 and Figure 2 shows the metabolic

pathways for the production of naringenin from

phenylalanine and tyrosine obtained based on the

stoichiometry predicted before, which have been

reported in previous studies35,36.

A solution for the overall reaction phenylalanine-

naringenin was found in the input with information from

organisms of the fermentation process, where the

OstStoic algorithm generated a five-step path (reactions:

R00697, R02255, R08815, R01613, and R02446, see

Table 3), completely complying with the stoichiometry

of the reaction.

Table 2. LB metabolites composition

Chemical formula22

Name22,25 ΔGf (kcal/mol)22 Charge23

O2 Oxygen 3.9197 0 CO2 Carbon dioxide -92.26 0 H2O Water -56.687 0 NH3 Ammonium -18.97 1 C9H11NO2 L-phenylalanine -51.05 0 C9H11NO3 L-tyrosine -90.53 0 C15H12O5 Naringenin -113.81 0 C2H3O2 Acetate -88.29 -1 C3H7NO2 L-alanine -87.92 0 C6H12N2O4S2 Cystine -159.61 0 C6H9N3O2 Histidine -45.63 0 C6H15N2O2 Lysine -84.04 1 C5H8NO2 L-Proline -62.54 -1 C11H12N2O2 L-Tryptophan -25 0 C6H15N4O2 Arginine -73.77 1 C5H8NO4 L-Glutamate -164.13 -1 C6H13NO2 L-Isoleucine -84.87 0 C5H11NSO2 L-Methionine -75.91 0 C3H7NO3 L-Serine -124.15 0 C4H6NO4 L-Aspartate -165.75 -1 C2H5NO2 Glycine -87.73 0 C3H7NO2 L-Leucine -84.87 0 C4H9NO3 L-Threonine -124.34 0 C5H11NO2 L-Valine -86.49 0 C3H7NSO2 Thiamine 52.18 1 C17H20N4O6 Riboflavin -110.72 0 C9H16NO5 Pantothenate -188.54 -1 C8H11NO3 Pyridoxine -67.43 0 C10H15N2SO3 Biotin -109.02 -1 C19H21N7O6 Tetrahydrofolate -113.97 -2 C6H4NO2 Niacin -36.89 -1

8

For the overall reaction of tyrosine-naringenin,

no solution was obtained from this input, because no

fermentation organism has a reaction capable of turning

tyrosine into p-coumaric acid. From the input of

information of the KEGG database, a solution was found

for each overall reaction. For the phenylalanine-

naringenin reaction, the same five-step pathway was

generated as for the fermentation database, while for the

tyrosine-naringenin reaction, a four-step path composed

of the reactions was generated (R00737, R01616,

R01613, and R02446, see Table 3).

Associated genes and enzymes to the pathways

predicted. In the Table 3 eight reactions belong to the

two predicted pathways are observed. Enzymes and

genes associated are also showed. For the selection of

genes associated with the catalytic enzymes of reactions

belonging to the predicted pathways, the following

criteria were used:

• The gene has been expressed in E. coli.

• The reported Michaelis Menten km constant

must be small, with an order of

magnitude 𝑥10−2.

• Gene must have mutations that can contribute

to the production of naringenin.

Figure 1. Metabolic pathway L-phenylalanine to naringenin. This pathway is composed of reactions PHETA1

(Phenylalanine transaminase), PAL/TAL1 (phenylalanine ammonia-lyase), CNL (p-coumaroyl CoA ligase), CINCOA

(cinnamic acid 4-hydroxylase), CHS (naringenin chalcone synthase) and CHI (chalcone isomerase). The circles and arrows

represent metabolites and reactions respectively. The names associated with the abbreviations used in this figure can be

found in supplementary material 3.

Figure 2. Metabolic pathway L-tyrosine to naringenin. This pathway is composed of reactions TYRTA (Tyrosine

transaminase), PAL/TAL2 (phenylalanine/tyrosine ammonia-lyase), 4CL (4-coumarate—CoA ligase), CHS (naringenin

chalcone synthase) and CHI (chalcone isomerase). The circles and arrows represent metabolites and reactions respectively.

The names associated with the abbreviations used in this figure can be found in supplementary material 3.

The first parameter is the most important because

it guarantees that the chosen gene can be expressed in E.

coli. The second one is related to the reaction rate and

indicates the affinity that the enzyme has for the

substrate and the last parameter is relevant when genes

with mutations that can have a positive impact on

naringenin biosynthesis are included (See Table 3).

Once the genes associated to the enzymes of each

reaction were found, the tyrosine-naringenin pathway

was chosen, this being the shortest, four steps to produce

the flavonoid, which facilitates the heterologous

expression of the genes in a bio-factory such as E. coli

(Table 4).

FBA and OptKnock. After the selection of

genes and enzymes, an FBA was carried out for the

pathway tyrosine-naringenin, using a model for E. coli

ATCC 8739 to verify the viability of the pathways in this

organism. FBA was performed under aerobic conditions

and with a glucose uptake of -10 mmol*gDW-1 *h-1. The

objective function "biomass" of the model obtained an

optimum value of 0.8935 mmol*gDW-1 *h-1 when there

is no production of naringenin (without the introduction

of the pathway).

9

Table 3. Reactions, enzymes, and genes belonging to the predicted pathways.

ID-RXN EC Number Enzyme Name Gene Organism Km Present in the fermentation

R00697 4.3.1.24 4.3.1.25

phenylalanine ammonia-lyase (PAL) phenylalanine/tyrosine ammonia-lyase (PAL/TAL)

BoPAL4 Bambusa oldhammi

0.09737 No

R02255 6.2.1.12 4-coumarate---CoA ligase (4CL)

Os4CL1 Oryza sativa 0.009438 No

R08815 1.14.14.91 cinnamate 4-hydroxylase39 ------------ ------------- ------------- No

R01616 6.2.1.12 4-coumarate---CoA ligase (4CL)

Q274H-F269L Solanum

lycopersicum 0.03640 No

R01613 2.3.1.74 naringenin-chalcona sintase CHS Hypericum

androsaemum 0.004941

Lysinibacillus sphaericus

C3- 41, Bacillus

subtilis168, Bacillus

pumilus ATCC 7061 and

Bacillus clausii KSM-K16.

R02446 5.5.1.6 chalcona isomerase CHI Pueraria

montana var. Lobata42

N/A No

R00737 4.3.1.23 4.3.1.25

tyrosine ammonia-lyase (TYR) phenylalanine/tyrosine ammonia-lyase (PAL/TAL)

TAL, BoPAL4

Rhodobacter capsulatus, Bambusa oldhammi

0.01643, 0.09737

No

The four reactions that form the pathway (Figure

2) were added and a new FBA was executed (R00697,

R01616, R01613, and R02446, see Table 3). In this new

analysis, the biomass growth obtained an optimum value

of 0.893 mmol*gDW-1*h-1 and a naringenin flux of

0.00026 mmol*gDW-1*h-1, which represents around

0.029% with respect to biomass growth. As seen in

Figure 3 as the biomass varies, the naringenin

production also varies, showing a production inversely

proportional to the biomass, directly affecting the growth

of the organism.

In order to carry out the production of naringenin,

it is necessary to take into account its precursor

metabolites. These metabolites are E4P: Erythrose 4-

phosphate associated with the pentose phosphate

pathway, PEP: phosphoenolpyruvate associated with

glycolysis and gluconeogenesis and the production of

acetyl coa. Malonyl coa is derived from acetyl coa. E4P

and PEP are precursors for the generation of the

shikimate pathway, essential for the formation of p-

Coumaroyl-CoA that joined with three molecules of

malonyl coa to react and produce naringenin chalcone,

four molecules of CoA and three molecules of CO2, a

reaction catalyzed by the enzyme chalcone synthase

(CHS). Naringenin chalcone is the direct precursor of

naringenin, which is transformed through the enzyme

chalcone isomerase (CHI)15. The pathways involved in

the production of naringenin are very important because

these are the ones that redirect their fluxes in order to

make this production feasible, decreasing or increasing

the flux in other pathways and reactions of the organism,

thus changing its phenotype.

To optimize the production of naringenin it was

necessary to force the strain to adapt the yield of this

product to a cellular objective such as the biomass,

establishing a relationship between the reactions

EX_naringenin (hypothetical exchange reaction) and

biomass44.

Once the production of naringenin was obtained

in the model, an optKnock analysis was carried out, with

the objective of maximizing this production while

maximizing the biomass of the model. The secretion of

a product can be coupled to the growth of biomass if its

precursor provides an essential component of the

biomass (as is the case of tyrosine) and if pathways that

metabolize the desired product are eliminated. Optknock

finds genes that can be knockout, thus forcing the strain

to secrete the product in optimal growth.

For coupling the following equation was used:

𝒗𝒃𝒊𝒐𝒎𝒂𝒔𝒔 = 𝑿 𝒗𝑬𝑿_𝒏𝒂𝒓[𝒆] (47)

Where 𝑣𝐸𝑋_𝑛𝑎𝑟[𝑒] is the flow of the

naringenin exchange reaction, 𝑣𝑏𝑖𝑜𝑚𝑎𝑠𝑠 is the flow of

the biomass reaction and 𝑋 is a proportional constant.

This equation means that the flux of the

naringenin exchange reaction is X times the biomass

10

flux. For the run of optKnock, a group of 96 candidate

reactions to be eliminated, participants in the central

metabolism, the shikimate pathway and the synthesis

and degradation of tyrosine were chosen.

Table 4. Genes selected to produce naringenin from

tyrosine

Gene Name Gene Organism Mutation Ref.

PAL/TAL BoPAL4 Bambusa oldhammi F133H 37

4CL Q274H-F269L

Solanum lycopersicum

Q274H 40

CHS CHS Hypericum

androsaemum L263M-F265Y

41

CHI CHI Pueraria montana

var. Lobata C119A 42

Just after performing the coupling and in where

the value of X was found through OptKnock iterations

where the value that obtained the highest fluxes for

biomass and naringenin production was chosen. Which

was X=1/7. With this result, a new FBA was carried out,

where a flux of 0.1231 mmol*gDW-1*h-1 of naringenin

was obtained by optimizing the biomass flux (0.8616

mmol*gDW-1*h-1).Increasing the flux of naringenin,

but decreasing the flux of biomass compared to the

previous results. In consequence, the pathways involved

in the naringenin production increase their fluxes in the

same way that Ex_nar[e] reaction flux. Also, 61

reactions were affected after performing the coupling in

the FBA, which mostly are transport reactions (37%) and

others belong to tyrosine, tryptophan and phenylalanine

metabolism (15%), glycerophospholipid metabolism

(15%), exchange reactions (15%), prosthetic group and

alternative carbon (12%) and cofactors metabolism

(10%).

Other effects related to coupling were the

generation of extreme fluxes due to possible

Thermodynamically Infeasible Cycles (TIC) and

activation and deactivation of the reaction. ICTs lead to

non-zero cycles without communication with the system

and the external environment, generating errors in

solving the optimization problem involved in the FBA45.

As such, there cannot be a net flow around a closed cycle

in a steady-state network46. The extreme fluxes are

directly related mainly to transport reactions, while the

functions performed by the reactions that were

deactivated are carried out by means of alternative

reactions or pathways with the aim of seeking balance

within the cell, thus manifesting a different phenotype

than that obtained before coupling. The reactions

involved in the exchange and transport of h+, Fe3 +, in

oxidative phosphorylation in the periplasm and in the

citric acid cycle were activated or deactivated due to

coupling. In addition, other reactions such as the

exchange and transport of Fe2 +, CO2 and H2O and the

formation of fumarate from succinate were affected by

the coupling.

Once the proportion between biomass and

naringenin production fluxes was fixed in the model, the

Optknock analysis was executed. This analysis

generated three sets of reactions which, when knocked

out, can increase the biomass while increasing the

production of naringenin. Supplementary material 2.

The first set consists of the reactions CITL (Citrate lyase

EC-4.1.3.6), MALS (Malate synthase EC-2.3.3.9) and

PPS (Phosphoenolpyruvate synthase EC-2.7.9.2), the

first reaction belongs to the citric acid cycle, the second

is an anaplerotic reaction, and the latter is part of

glycolysis and glycogenesis, this set obtained a flux of

0.1267 mmol*gDW-1*h-1 of naringenin and biomass flux

of 0.887 mmol*gDW-1*h-1. The second set is composed

of the PFK reactions (Phosphofructokinase EC-2.7.1.11)

that are part of the glycolysis and glycogenesis, MALS

and PPS, this set obtained a flux of 0.1258 mmol*gDW-

1*h-of naringenin and biomass flux of 0.8809

mmol*gDW-1*h-. The last set contains the CITL and

MALS reactions, this set obtained a flux of 0.1267

mmol*gDW-1*h-1 of naringenin and biomass flux of

0.887 mmol*gDW-1*h-1. Supplementary material 1. The

fluxes for the production of biomass and naringenin

increased in comparison to the fluxes of these before

optimization for the three sets of reactions given by

Optknock.

Figure 3. Naringenin flux versus biomass

growth. This graph was obtained using an iterative FBA,

varying the stoichiometric coefficients of biomass

growth (variations of 0.05 mmol*gDW-1*h-1) to observe

the behavior of the naringenin flux.

Chemical production of apiforol and

antioxidant capacity assay. From naringenin

compound, a search was made for products that had a

beneficial effect on human health and, in turn, did not

11

have a commercial form. Due to this and after reviewing

the reactions downstream from naringenin in the

metabolism of the flavonoids, 16 EC numbers, 40

reactions and 31 metabolites associated with these were

found. From the review of scientific literature and

specialized databases, two metabolites were found that

met the first criteria, see methods, the first was the

metabolite 8-C-Glucosylnaringenin (C16492) and the

second was Apiforol (C12124).

Apiforol was selected to perform the chemical

synthesis since it met both parameters since currently no

commercial products have been reported from this, in

addition, its antidiabetic properties have been reported

16, while for 8-C-glucosylnaringenin it has not been

reported. They found health benefits. An additional

advantage of apiforol is that it is a direct derivative of

naringenin, through the action of the enzyme Flavanone

4-reductase EC: 1.1.1.234. Apiforol is a 3-

deoxyflavonoids just like luteophorol, known as flavan-

4oles47,48. Flavan-4oles are derived from alcohols and a

family of flavonoids and these compounds are colorless

that polymerize to form red flavophens49. The latter is

red substances soluble in alcohols and insoluble in water,

which can be extracted from the plant or result from the

treatment of tannin extracts with mineral acids such as

hydrochloric acid 50.

The reduction of naringenin was carried out using

the compound sodium borohydride and a metabolic

solution of naringenin. 10 mg of apiforol were obtained

from the reaction, this being a yellow compound.

Subsequently, apiforol were diluted in 5 ml of reagent

grade ethanol and stored at -80°C in dark conditions.

From this solution an aliquot of 50 µl was taken and

mixed with 300 µl of hydrochloric acid and 1 ml of

ethanol, immediately passing from a light-yellow color

to a strong red. Thus, confirming the presence of flavan-

4-ol apiforol.

After obtaining the apiforol, its antioxidant capacity

was measured by the FRAP method. The dose–

response characteristics of apiforol showing that

antioxidant activity is concentration-dependent in the

three concentrations. However, the measurements of

the 0.1 mg / ml curve show very high standard

deviations, which may be due to errors in these.

Figure 4 shows the rate of increase in absorbance

at 593 nm for 100 mmol/L solutions of apiforol. This

reaction does not reach a final point within the

monitoring period and a constant increase in absorbance

is observed throughout the time interval from the

sample/reagent mixture. To observe the variation of the

absorbance for each concentration with respect to the

calibration curves (FeSO4). Supplementary material 2.

The FRAP value for the time interval t was

obtained by the following equation51:

𝑭𝑹𝑨𝑷𝒕(𝑴) = (∆𝒂𝒕𝑭𝑰

∆𝒂𝒕𝑭𝒆𝟐+) ∗ 𝟏𝟎−𝟓 (48)

Figure 4. FRAP reaction kinetics. Rate of increase in absorbance at 593 nm for 100 mmol/L solutions of apiforol

compound.

12

Where:

∆𝑎𝑡𝐹𝐼: Change in absorbance after the time interval t, in

relation to the apiforol tested at a defined concentration.

∆𝑎𝑡𝐹𝑒2+: Change of absorbance in the same time

interval, for ferrous sulfate at the same concentration.

Figure 5. Total antioxidant capacity of apiforol by FRAP

method for concentrations 0.1, 0.2 and 0.3 mg/ml to 8

min.

The FRAP values for apiforol in this study were

4x10-5 M (40 μmol/L), 8x10-5 M (80 μmol/L) and

4.5x10-4 M (450 μmol/L) for concentrations of 401

μmol/L, 693 μmol/L and 981 μmol/L of apiforol and

FeSO4 respectively. These results were for the time

interval of the first four-minutes. For the total study time

(8 minutes), the FRAP values obtained were 8x10-5 M

(80 μmol/L), 1.2x1045 M (120 μmol / L) and 6x10-4 M

(600 μmol/L) for the concentrations of 401 μmol/L, 693

μmol/L and 981 μmol/L of apiforol (see Figure 5).

3. Discussion

Table 3 shows that the only enzyme found in the

organisms of the process of fermentation of cocoa seeds

is the naringenin-chalcone synthase EC: 2.3.1.74,

present in the organisms Lysinibacillus sphaericus C3-

41, Bacillus subtilis168, Bacillus pumilus ATCC 7061

and Bacillus clausii KSM-K16. These organisms are not

classified in the three groups of organisms most relevant

to the process (Y, ALB, and AAB), however, several

Bacillus strains are important in the first days of

fermentation since they have been related to the

degradation of the pectin present in the mucilage of

cocoa seeds52.

Also, could be observed that almost all the

enzymes (Table 3) are found in the genomes of plants

Theobroma cacao and Arabidopsis thaliana, except the

enzymes related to the EC numbers: EC: 4.3.1.25

(PAL/TAL) and 4.3.1.23 (TAL). TAL is found in the

genomes of bacteria such as Ectothiorhodospira

halophila, Actinosynnema mirum, Rhodobacter

sphaeroides, Salinibacter ruber and Cupriavidus

metallidurans, and in yeast as Rhodotorula glutinis.

While, PAL/TAL is present in the genome of plants

Oryza sativa japónica, Oryza brachyantha, and

Brachypodium distachyon among others.

Because the PAL/TAL enzyme has been

previously reported with the particularity of

transforming both tyrosine and phenylalanine, previous

research has shown that the BoPAL4 gene, associated

with this enzyme, could have a double activity for this

enzyme. For this reason, this gene was chosen to be

expressed in E. coli. The rest of the genes were chosen

because of a small km, which means that the enzyme-

substrate complex is stable and the tendency to form

predominates, meaning that there is a high affinity of the

enzyme for the substrate, considering the mutations as

shown in Table 4.

When introducing the chosen metabolic pathway

and carrying out the FBA and optKnock analyzes and

comparing the two phenotypes, with and without

coupling, changes were evidenced in 61 reactions of the

model (of a total of 2875 reactions), which were

activated, deactivated or there was an increase or

decrease in their activity. The sub-systems with the

greatest number of affected reactions are the transport

ones (inside the membrane and through porins), the

alternate metabolism of the carbon, the metabolism of

the glycerophospholipids, and the biosynthesis of the

groups of cofactors and prosthetics, the exchange

reactions and of the metabolism of tyrosine, tryptophan,

and phenylalanine.

From the transport subsystem, the reaction

GLUt4pp (Na+/glutamate symport (periplasm))

transporting Na+ from the periplasm to the cytosol was

deactivated. Several proton transport reactions were also

activated, in which abrupt activity was observed,

probably linked to non-feasible thermodynamic cycles,

in which reactions were observed with fluxes that

oscillated the values of the lower and upper limits of the

reactions (LB = -1000 and UB = 1000) both symport and

antiport pathways. Another reaction that presented

extreme flows was the transport of carnitine between the

cytosol and the periplasm, as well as the consumption of

water and the production of CO2 from and to the

environment, respectively within the network. In

addition, the transport of Fe2 + and Fe3 + ions were

activated through the diffusion pathway with

considerable reaction flows.

In the subsystem of alternative carbon

metabolism, six reactions were deactivated, of which

ACCOAL (Acetate-CoA ligase (ADP-forming)) and

PPCSCT (Propanoyl-CoA: succinate CoA-transferase),

13

involved in the formation of ADP and succinyl-CoA,

had an abrupt drop in their flows. For the

glycerophospholipid metabolism subsystem, three

ACPPAT160 (Acyl-(acyl carrier protein):phosphate

acyltransferase (C16:0)), ACPPAT161 (Acyl-(acyl

carrier protein):phosphate acyltransferase (C16:1)) and

G3PAT181 (Glycerol-3-phosphate acyltransferase (C18

1)) reactions were turn off and three other ACPPAT181

(Acyl-(acyl carrier protein):phosphate acyltransferase

(C18:1)), G3PAT160 (Glycerol-3-phosphate

acyltransferase (C16:0)), and G3PAT161 (Glycerol-3-

phosphate acyltransferase (C16:1)) were activated,

involved with palmitoyl-ACP (n-C16: 0ACP), cis-

hexadeca-9enoyl- [acyl-carrier-protein] (n-C16: 1), cis-

octadeca-11-enoyl- [acyl-carrier protein] (n-C18: 1) in

phosphate transport and h+, activation, and deactivation

indicates that alternate reactions were activated to

transport the metabolites involved.

In the biosynthesis subsystem of the cofactor and

prosthetic groups, the LIPOCT (Lipoyl(octanoyl)

transferase), MECDPDH2 (2C-methyl-D-erythritol 2,4

cyclodiphosphate dehydratase), and NPHS (Naphthoate

synthase) reactions were turned off and the MECDPDH5

(2C-methyl-D-erythritol 2,4 cyclodiphosphate

dehydratase) and OCTNLL (Octanoate non-lipoylated

apo domain ligase) reactions were activated, the

activation and deactivation indicate that alternate

reactions were activated for the production of Octanoate

(protein-bound) and 1-hydroxy-2-methyl-2- (E) -butenyl

4-diphosphate.

The subsystem of exchange reactions involves

the aforementioned, in the case of proton flux, the proton

exchange reaction EX_h_e, was turned off showing that

the need for protons for the metabolism of the organism

is so overwhelmed that there is no export to the outside

of the cell of this cofactor. In the case of the exchange

reactions related to Fe2+ and Fe3+, it is observed that

the consumption of Fe2+ from the medium increased

about seven times and the production of Fe3+ in the

medium increased equally around 7 times. In the

metabolism subsystem of tyrosine, tryptophan and

phenylalanine, it was observed that the fluxes that make

up the shikimate path increased its production flux from

0.3325 to 0.4435 mmol*gDW-1*h-1, which caused an

increase in the flux of the TYRTA reaction, from -0.12

to -0.24 mmol*gDW-1*h-1, which is the direct couple

reaction to the predicted heterologous pathway. The flux

of naringenin production went from 0.00026 to 0.1267

mmol*gDW-1*h-1 when performing the coupling.

From the tests carried out in the optKnock

analysis, we observed that from the ratio 1: 7 (1 mmol-

naringenin*gDW-1*h-1 per 7 mmol*gDW-1*h-1 of

biomass), the relation is satisfactorily coupled, meaning

that the production of naringenin is connected to the

growth of biomass. Of the results thrown by OptKnock,

both the set 1 and the set 2 show a naringenin production

and an equal growth rate, which are greater than those

shown for the set 2. However, the set 1 has seven genes

involved, four for CITL (citE, citD, citX and citF), two

for MALS (aceB, glcB) and one for PPS (ppsA), while

the set 3 has six genes. Also, the set 2 has five genes, two

for PFK (pfkA, pfkB), and two for MALS (aceB, glcB)

and one for PPS (ppsA).

By performing an FBA for each reaction set

thrown by the OptKnock analysis, the following results

were obtained: Set 1. PPS, MALS, and CITL were

knocked out; the knockout of the PPS reaction prevented

the formation of PEP from pyruvate, directing the

pyruvate flow to the formation of actetyl coa. By

knocking out the MALS and CITL reactions, the

additional expense of acetyl coa in the TCA is avoided,

so the flows are concentrated in the production of acetyl

coa, a direct precursor of the malonyl coa molecules

necessary for the formation of the naringenin chalcone.

Set 2. PFK, MALS, and PPS were knocked out; the

knockout of the PFK reaction led to the knockout of the

FBA reaction, redirecting the flow through the F6PA

reaction (Fructose 6-phosphate aldolase) and thus

allowing the formation of the PEP and 4EP

concentrating the fluxes of these metabolites towards the

shikimate pathway, directing them towards the

formation of p-coumaroyl coa, also necessary in the

formation of naringenin chalcone. Set 3. CITL and

MALS were knocked out. From the above it can be

concluded that the three sets sought to improve the

formation of malonyl coa and p-coumaroyl coa, direct

precursors of naringenin, thus improving the formation

of this metabolite. From the above and with the objective

of generating the least number of disturbances within the

network, the most appropriate set to improve naringenin

production was set 3 formed by the MALS and CITL

reactions.

OptKnock results show that naringenin

production increases as the growth rate increases

(Supplementary material 1), since the direct precursor in

this case, of the flavonoid naringenin is the amino acid

tyrosine, which is part of the biomass objective function.

Therefore, by optimizing the growth rate, tyrosine

production will be optimized together, forcing the strain

to improve the secretion of the product of interest30. It

should be considered that the relationship used for the

coupling was estimated through several iterations in

OptKnock since no information was available on the

relationship between the growth rate and the production

of naringenin of any organism, neither modified nor

wild. For this reason, they must carry out studies that

give this information and adjust the model to these

results.

14

From the chemical synthesis of naringenin to

produce apiforol, a yellow substance was generated, as

confirmed by Khalil et al., 201250. In the confirmation of

the presence of apiforol in the product generated from

the synthesis, a substance was obtained between rose and

red, which confirms that flavan 4-ol was obtained.

The FRAP values for apiforol in this study were

4x10-5 M (40 μmol/L), 8x10-5 M (80 μmol/L) and

4.5x10-4 M (450 μmol/L) for concentrations of 401

μmol/L, 693 μmol/L and 981 μmol/L of apiforol and

FeSO4 respectively. These results were for the time

interval of the first four minutes. Previously, it was

reported that for naringenin under similar conditions,

except for the concentration (10 μmol/L), the FRAP

value was 0 umol/L for the 4 minute time interval and

activity was observed at 60 minutes, with a FRAP value

of 0.5 μmol/L, showing a very low activity, reflected in

a very low absorbance change at a wavelength of 595

nm51. The comparison of our results and those shown in

the previous study for naringenin show considerable

variations, since it is observed that apiforol has a higher

antioxidant capacity than naringenin in a shorter time

interval; however, it is suggested to perform the FRAP

test for two compounds under the same conditions and

to reinforce the results of antioxidant capacity with other

methods such as ABTS+, DHHP, among others.

4. Conclusions

It was possible to predict optimal metabolic

pathways to produce naringenin from the metabolic

information of the organisms involved in cocoa seed

fermentation and the KEGG database, using the

OptStoic algorithm, which had already been previously

reported by the researchers 35,36.

One of the cases in which artificial pathways or

de novo synthetic pathways are used is when the product

of interest is generated by a higher organism such as

plants and whose production is not profitable; such is the

case discussed in this document (naringenin production).

Taking this approach, it is formulated to insert the

pathways predicted by the algorithm proposed by the

researchers A. Chowdhury and C. Maranas19, in the

microorganism model E. coli, specifically the pathway

from L-tyrosine, using the proposed heterologous genes

(BoPAL4, Q274H-F269L, CHS, and CHI) (Table 4).

From the FBA, it was found that the model alone

produced a very low naringenin flow, less than 0.1%

when introducing the heterologous pathway, so it was

necessary to make an adjustment to the model, in which

the production was coupled flavonoid the rate of growth

(biomass target function), generating a phenotype able to

produce naringenin with a ratio of 1: 7, forcing the strain

by this coupling. It is suggested that this coupling be

adjusted according to the results by heterologous

expressing the pathway in E. coli, looking for a

relationship between the production of naringenin and

the production of biomass of the organism.

The abrupt variations in the protonation,

phosphate and ferric compounds flow must be

considered since these are fundamental in the catalytic

and stoichiometric processes of the organism.

From the analysis of the results obtained by

optKnock, the best set of reactions to knock out,

depending on the production of naringenin and the

number of genes to be eliminated is the set 3 composed

of the CILT and MALS reactions, which increases the

production flux of naringenin in almost 3%.

It was possible to synthesize the apiforol

compound from naringenin and it was demonstrated by

the FRAP assay, that it has antioxidant activity, which

must be validated by other methods and it is suggested

to perform this same assay under the same conditions for

the naringenin compound.

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18

Supplementary 1. Reaction sets found with

OptKnock.

Supplementary 2. Antioxidant capacity by apiforol

concentrations compared to the FeSO4 calibration

curve

19

Supplementary 3. Abbreviations used in predicted metabolic pathways

abbreviation Name

akg 2-Oxoglutarate

glu__L L-Glutamate

phe__L L-Phenylalanine

phpyr Phenylpyruvate

nh3 Ammonia

cinnm Trans-Cinnamate

coa Coenzyme A

atp ATP

cinnmcoa Cinnamoyl-CoA

ppi Diphosphate

amp AMP

nadph Nicotinamide adenine dinucleotide phosphate - reduced

o2 O2

h H+

nadph Nicotinamide adenine dinucleotide phosphate - reduced

h2o H2O

coucoa 4-Coumaroyl-CoA

malcoa Malonyl-CoA

narcha naringenin chalcone

co2 CO2

nar naringenin

34hpp 3-(4-Hydroxyphenyl)pyruvate

tyr__L L-Tyrosine

nh4 Ammonium

Th4cinnm Trans 4 Hydroxycinnamate