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CHLOROGENIC ACID AND ITS RELATIONSHIP WITH LIGNIN...
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UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE BIOLOGIA Nathalia Volpi e Silva CHLOROGENIC ACID AND ITS RELATIONSHIP WITH LIGNIN BIOSYNTHESIS ÁCIDO CLOROGÊNICO E SUA RELAÇÃO COM A BIOSSÍNTESE DE LIGNINA CAMPINAS - SP 2019
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UNIVERSIDADE ESTADUAL DE CAMPINAS
INSTITUTO DE BIOLOGIA
Nathalia Volpi e Silva
CHLOROGENIC ACID AND ITS RELATIONSHIP WITH LIGNIN BIOSYNTHESIS
ÁCIDO CLOROGÊNICO E SUA RELAÇÃO COM A BIOSSÍNTESE DE LIGNINA
CAMPINAS - SP
2019
NATHALIA VOLPI E SILVA
CHLOROGENIC ACID AND ITS RELATIONSHIP WITH LIGNIN BIOSYNTHESIS
ÁCIDO CLOROGÊNICO E SUA RELAÇÃO COM A BIOSSÍNTESE DE LIGNINA
Thesis presented to the Institute of Biology of
the University of Campinas in partial fulfillment
of the requirements for the degree of Doctor in
Genetic and Molecular Biology in the area of
Plant Genetics and Breeding.
Tese apresentada ao Instituto de Biologia da
Universidade Estadual de Campinas como parte
dos requisitos exigidos para obtenção do Título
de Doutor em Genética e Biologia Molecular, na
área de Genética Vegetal e Melhoramento.
Supervisor / Orientador: Prof. Dr. Paulo Mazzafera
Co-supervisor / Co-Orientador: Prof. Dr. Igor Cesarino
ESTE ARQUIVO DIGITAL CORRESPONDE À VERSÃO FINAL DA
TESE DEFENDIDA PELA ALUNA NATHALIA VOLPI E SILVA,
ORIENTADA PELO PROF. DR. PAULO MAZZAFERA.
CAMPINAS - SP
2019
Agência de fomento: FAPESP
N° Processo: 2014/17831-5, 2016/15834-2
Nº processo:0
Agência de fomento: Capes
N° Processo: 001
Nº processo:0
Campinas, 31de julho de 2019
EXAMINATION COMMITTEE
Banca examinadora
Dr. Paulo Mazzafera (Supervisor/Orientador)
Dr. Paula Macêdo Nobile
Dra. Sara Adrian Lopez de Andrade
Dr. Douglas Silva Domingues
Dr. Michael dos Santos Brito
Os membros da Comissão Examinadora acima assinaram a Ata de Defesa, que se encontra
no processo de vida acadêmica do aluno.
ACKNOWLEDGMENT
I would like to thank the São Paulo Research Foundation (Fundação de Amparo à
Pesquisa do Estado de São Paulo) Grant (Processo) nº 2014/17831-5, FAPESP and n°
2016/15834-2, FAPESP for the grant/fellowship and all financial support to develop this thesis.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior – Brasil (CAPES) – Finance Code 001.
I am profoundly grateful to my supervisor Dr. Paulo Mazzafera and my co-supervisor
Dr. Igor Cesarino for all knowledge shared and patience to guide me through this journey. I am
also extremely grateful to Dra. Nicola J Patron for receiving me in her laboratory at Earlham
Institute (Norwich – UK) and teach me about genome editing.
My sincere thanks to Tatiane Gregório, Felipe Tolentino, Ewerton Ribeiro, Dr. Oleg
Raitskin, Dra. Juliana Mayer and Dr. Eduardo Kiyota for helping me with my experiments when
I needed. I could not forget to thank Rafaela Bulgarelli, Dra. Sarah Caroline Ribeiro and Uiara
Romero Souza for all the help in taking care of my plants while I was in maternity leave.
Dr. Adilson Domingues Junior, Dr. Franklin Magnum Silva, Dr. Luciano Pereira, and
Dra. Flávia Shimpl also gave me invaluable help with my writing and academic talk. I also
would like to thank all LAFIMP’s team for friendship and support.
My research would have been impossible without the support of my family: my
husband, my parents, my daughter, my sister. You have always been there for me with unfailing
support and continuous encouragement, thank you. My husband, my mother and my mother-
in-law help taking care of my daughter Laura were essential while I was writing my thesis.
ABSTRACT
Phenylpropanoids are compounds derived from phenylalanine and are involved in several
aspects related to the defense of biotic and abiotic stresses. One of the phenylpropanoids present
in most plants is chlorogenic acid (CGA). CGA biosynthesis is mediated by the enzyme
hydroxycinnamoyl quinate transferase (HQT). Although never proved, some papers have
suggested that the CGA pool could be related to lignin biosynthesis in plants. Another enzyme,
hydroxycinnamoyl shikimate transferase (HCT), appears to be involved with both lignin and
CGA pathway. Like HCT, HQT uses p-coumaroyl CoA for the formation of the
hydroxycinnamoyl shikimate or hydroxycinnamoyl quinate esters, respectively. In addition,
recently the enzyme caffeoyl shikimate esterase (CSE) has been described as involved in the
conversion of caffeoyl shikimate to caffeic acid, which is subsequently converted to caffeoyl
CoA in lignin route. CSE shares the substrate with the HCT enzyme, thus suggesting that a
change in its expression may interfere not only with lignin metabolism, but also with CGA.
Because they present common intermediates, it is possible that CGA can act as a donor of
carbon skeletons for lignin biosynthesis. In Chapter 1 we brought a review discussing the
interconnection among the main genes involved in CGA and lignin interdependency, HCT,
HQT, and CSE. In Chapter 2 we focused on the relationship between the genes CSE and HCT,
bringing important data to reinforce the importance of shikimate shunt in both pathways. In
Chapter 3 we constructed and validated CRISPR/Cas9 constructions to genome edit HCT, CSE,
and CCoAOMT aiming the development of tobacco stable mutants. The construction of mutant
and double mutants overexpressing and silencing the HCT, HQT and CSE genes may help to
clarify the nature of this interdependence between the CGA pool and lignin, as well as to
validate the role of the CSE enzyme as a common component in the lignin pathway.
Bioinformatics analyses identified four putative isoforms of the HCT gene and two of CSE in
Nicotiana tabacum, the species chosen for this study. In order to obtain mutants for these genes
we designed several transformation constructions: pCaMV35S::CSE (CSE overexpression),
pCaMV35S::HCT (HCT overexpression); pCaMV35S::HQT (HQT overexpression);
pCaMV35S::amiRNACSE (CSE downregulation),
pCaMV35S::HCT::pCsVMV::amiRNAHQT (HCT overexpression combined with HQT
downregulation); pCaMV35S::HQT::pCsVMV::amiRNAHCT (HQT overexpression
combined with HCT downregulation), and pCaMV35S::HCT::pCsVMV::amiRNACSE (HCT
overexpression combined with CSE downregulation). CSE silencing plants (amiCSE) showed
severe dwarfed phenotype and did not produce any descendants indicating the importance of
CSE in plant normal development. On the other hand, HCTamiCSE and CSE developed
normally and were carried to generation T1 where it was conducted further analyses. The
mutants were assayed for phenotype, gene expression, lignin, plant cell wall polysaccharides,
saccharification, and phenolic profiling. The plants analyzed showed no alteration in the
composition of lignin, but presented alterations in the metabolism of chlorogenic acid,
especially the plants overexpressing CSE, indicating a probable role of CGA as carbon skeleton
of the lignin pathway. In addition, we also successfully constructed and validated vectors using
CRISPR/Cas9 tool for the CCoAOMT, CSE and HCT genes in tobacco leaves. Although several
studies suggest the interconnection between the lignin and chlorogenic acid routes, most of the
analyzes shown are in vitro. The fact that our mutants have the chlorogenic acid composition
affected strongly suggests that these pathways are interconnected and that CSE may play a
decisive role in the biosynthesis of chlorogenic acid in tobacco plants.
RESUMO
Fenilpropanóides são compostos derivados da fenilalanina e estão envolvidos em vários
aspectos relacionados à defesa de plantas. Alguns desses fenilpropanóides são os ácidos
clorogênicos (CGA). A biossíntese de CGA é mediada pela enzima hidroxicinamoil quinato
transferase (HQT). Embora nunca provado, alguns trabalhos sugerem que o pool de CGA
poderia estar relacionado com a biossíntese de lignina. Outra enzima, a hidroxiciamoil
chiquimato esterase (HCT), parece estar envolvida nas rotas de biossíntese de lignina e ácido
clorogênico. Assim como a HCT, a HQT utiliza p-coumaoil CoA para formação de ésteres de
hidrocinamoil chiquimato ou hidroxicinamoil quinato, respectivamente. Além disso, a enzima
cafeoil chiquimato esterase (CSE) foi descrita como envolvida na conversão de cafeoil
chiquimato em ácido cafeico, o qual é convertido em cafeoil CoA. Dessa forma, CSE
compartilha o substrato com a enzima HCT, sugerindo que uma mudança em sua expressão
deva interferir não apenas no metabolismo de lignina, mas também no metabolismo de CGA.
Por terem intermediários em comum, é possível que haja interdependência entre essas vias, e
CGA possa atuar como doadora de esqueleto de carbono para a biossíntese de lignina. Desta
forma, este trabalho objetiva trazer mais informações a fim de entender a relação entre estas
duas vias de biossíntese. No Capítulo 1 trouxemos uma revisão com foco na relação entre os
principais genes envolvidos na interdependência entre CGA e lignina, os genes HCT, HQT e
CSE, com objetivo de conectar os dados disponíveis na literatura que tratam deste assunto. No
Capítulo 2 focamos na relação entre estas vias e os genes HCT e CSE, trazendo dados
importante que reforça a importância do braço da rota que utiliza chiquimato tanto para lignina
como para CGA. No Capítulo 3 construímos e validamos vetores para edição de genoma os
genes HCT, CSE e CCoAOMT com objetivo de futuramente desenvolvermos plantas mutantes
para estes genes via CRISPR/Cas9. A construção de mutantes e duplos mutantes super-
expressando e silenciando os genes HCT, HQT e CSE pode contribuir para esclarecer a natureza
dessa interdependência entre o pool de CGA e lignina, assim como validar o papel da enzima
CSE como um componente da via de lignina em Nicotiana tabacum. Análises de bioinformática
identificaram quatro isoformas putativas do gene HCT e duas do gene CSE em N. tabacum, a
espécie escolhida para estudo. Com objetivo de obtermos mutantes para estes genes foram
desenhadas várias construções para transformação estável: pCaMV35S::CSE (CSE super-
expressão), pCaMV35S::HCT (HCT super-expressão); pCaMV35S::HQT (HQT super-
expressão); pCaMV35S::amiRNACSE (CSE silenciamento),
pCaMV35S::HCT::pCsVMV::amiRNAHQT (HCT super-expressão combinada com HQT
silenciamento); pCaMV35S::HQT::pCsVMV::amiRNAHCT (HQT super-expressão
combinada com HCT silenciamento), e pCaMV35S::HCT::pCsVMV::amiRNACSE (HCT
super-expressão combinada com CSE silenciamento). As plantas silenciadas para o gene CSE
(amiCSE) apresentaram comprometimento severo do desenvolvimento e não produziram
descendentes indicando a importância da CSE para o desenvolvimento normal em N. tabacum.
Em contrapartida, as linhagens de HCTamiCSE e CSE não apresentaram alteração do fenótipo.
Essas plantas foram cultivadas até T1 e então submetidos às seguintes análises: fenotípica,
expressão gênica, lignina, polissacarídeos de parede celular, sacarificação e perfil fenólico. As
plantas analisadas não apresentaram alteração da composição de lignina, mas apresentaram
alteração no metabolismo de CGA, especialmente as plantas super-expressando o gene CSE,
indicando um provável papel de CGA como esqueleto de carbono para o metabolismo de
lignina. Além disso, nós também construímos e validamos vetores utilizando a ferramenta de
edição de genoma via CRISPR/Cas9 para os genes HCT, CSE e CCoAOMT em folhas de N.
tabacum. Apesar de vários estudos sugerirem uma interconexão entre as vias de lignina e CGA,
a maior parte das análises foram feitas in vitro. O fato dos nossos mutantes terem a composição
de CGA afetada sugere fortemente que essas vias são interconectadas, e que CSE tem um papel
decisivo na via de biossíntese em N. tabacum.
ABBREVIATION LIST
CGA – chlorogenic acid
5-CQA – 5- caffeoyl quinate
di-CQA – 3,5-dicaffeoyl quinate
CCT – chlorogenate:chlorogenate transferase
PAL – phenylalanine ammonia-lyase
C4H – cinnamate 4-hydroxylase
C3H – p-coumarate 3-hydroxylase (ascorbate peroxidase)
4CL – 4-coumarate:CoA ligase
HCT – hydroxycinnamoyl-CoA:shikimate hydroxycinnamoyl transferase
HQT – hydroxycinnamoyl-CoA:quinate hydroxycinnamoyl transferase
C3’H – p-coumaroyl shikimate 3′-hydroxylase (CYP98)
CSE – caffeoyl shikimate esterase
CCoAOMT – caffeoyl-CoA 3-O-methyltransferase
UGT84 – UDP-glucoside transferase
HCGQT – hydroxycinnamoyl D-glucose:quinate hydroxycinnamoyl transferase
H – p-hydroxyphenyl
G – guaiacyl
S – syringil
CCR – cinnamoyl-CoA reductase
CRISPR/Cas9 – Cluster Regulatory Interspaced Short Palindromic repeats/ Associated
protein 9 system
amiRNA – artificial microRNA
WT – Wild Type
pCaMV35S – Cauliflower Mosaic Virus 35S promoter
pCsVMV – Cassava vein mosaic virus promoter
UPLC- MS/MS – Ultra-Performance Liquid Chromatography coupled to a mass
spectrometer
PCA – Principal Component Analyses
PC1 – Principal Component 1
PC2 – Principal Component 2
ORF – Open Read Frame
CDS – Coding Sequence
HCBPT – Anthranilate N-hydroxycinnamoyl/ benzoyltransferase
DAT – deacetylvindoline 4-O- acetyltransferase
CNRQ – Calibrated Normalized Relative Quantities
CA – Caffeic acid
LMID – Lignin modification induced dwarfism
PAM – Protospacer adjacent motif
gRNA – guide RNA
tracrRNA – trans acting RNA
crRNA – CRISPR RNA
trugRNA – truncated guideRNA
MoClo – Golden Gate Modular Cloning
PCR/RE – Restriction enzyme site loss-based PCR
NptII – neomycin phosphotransferase
pNOS – Nopaline synthase promoter
TNos – Nopaline synthase terminator
SpCas9h – Cas9 from Streptococcus pyogenes human códon optimized
T35S – 35S terminator
OsCald5H1 – conipheraldehyde 5-hydrolase
SNP – Single Nucleotide Polymorphism
SUMMARY
INTRODUCTION .................................................................................................................... 14
Chlorogenic acids biosynthesis ............................................................................................. 17
Lignin biosynthesis ............................................................................................................... 21
Relationship with lignin and chlorogenic acids biosynthetic pathways ............................... 23
Chapter 1
Abstract ..................................................................................................................................... 28
Chapter 2
1. Introduction .......................................................................................................................... 30
2. Material and Methods ........................................................................................................... 32
2.1. Sequence analysis .......................................................................................................... 32
2.2. Vector assemble and amiRNA design ........................................................................... 33
2.3. Plant Transformation ..................................................................................................... 34
2.4. qRT-PCR analyses ......................................................................................................... 35
2.5. Phenolic profiling .......................................................................................................... 36
2.6. Lignin quantification ...................................................................................................... 36
2.7. Plant cell wall polysaccharides and saccharification ..................................................... 37
2.8. Morphological and histochemical analyses ................................................................... 37
2.9. Statistical analyses ......................................................................................................... 37
3. Results .................................................................................................................................. 38
3.1. Bioinformatic analysis ................................................................................................... 38
3.1.1. HCT ......................................................................................................................... 38
3.1.2. CSE.......................................................................................................................... 39
3.2. Vector assembly and amiRNA design ........................................................................... 40
3.3. Plant Transformation ..................................................................................................... 46
3.3. Gene expression in different tissues of WT and mutants of tobacco ............................. 47
3.3.1. Different tissues in Wild Type Plants ..................................................................... 47
3.3.2. qRT-PCR screening from double and single mutants ............................................. 49
3.5. Phenolic Profiling .......................................................................................................... 53
3.6. Lignin content and composition .................................................................................... 56
3.7. Plant cell wall polysaccharides and saccharification ..................................................... 58
3.8. Morphological and histochemical analyses ................................................................... 60
3.9. Pearson correlation and network analyses ..................................................................... 64
3.10. Principal Components Analysis ................................................................................... 66
4. Discussion ............................................................................................................................. 68
4.1. Search of HCT and CSE alleles and expression profile ................................................. 68
4.2. Downregulation of CSE severely impact plant development ........................................ 69
4.3. HCT overexpression overcome cse dwarfism and CSE mutants accumulate CGA
without affecting lignin content ............................................................................................ 71
Chapter 3
1. Introduction .......................................................................................................................... 78
2. Material and Methods ........................................................................................................... 82
2.1. Target locus selection and sgRNA design ..................................................................... 82
2.2. Construct DNA assembly and multiplex targeting ........................................................ 82
2.3. Agroinfiltration: Development, test, and delivery of the constructions in tobacco leaves
.............................................................................................................................................. 83
2.4. Genotyping .................................................................................................................... 83
3. Results and Discussion ......................................................................................................... 83
3.1. Target locus selection and sgRNA design ..................................................................... 83
3.1.1. HCT ......................................................................................................................... 83
3.1.2. CCoAOMT .............................................................................................................. 86
3.1.3. CSE.......................................................................................................................... 89
3.2. Construct DNA assembly and multiplex targeting ........................................................ 90
3.3. Agroinfiltration and Genotyping ................................................................................... 96
4. Discussion ........................................................................................................................... 101
CONCLUSION ...................................................................................................................... 104
PERSPECTIVES .................................................................................................................... 106
REFERENCES ....................................................................................................................... 107
SUPPLEMENTARY INFORMATION ................................................................................. 124
ATTACHMENT ..................................................................................................................... 135
Attachment 1 ....................................................................................................................... 135
Attachment 2 ....................................................................................................................... 136
Attachment 3 ....................................................................................................................... 137
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INTRODUCTION
Plant cell wall, or lignocellulosic biomass, account for 70% of the biomass
produced worldwide and is therefore considered the most abundant renewable resource in the
world (Pauly and Keegstra, 2008; Mottiar et al., 2016). The cell wall is a component of the raw
material of various consumer goods such as wood, textiles, paper, films, explosives and biofuels
(Cosgrove, 2000; Neutelings, 2011). Despite the high economic and productive potential, the
use of lignocellulosic biomass feedstock is hampered by its chemical recalcitrance (Pauly and
Keegstra, 2008). The cell wall has a complex and dynamic structure and is composed mostly
of high molecular weight polysaccharides, glycosylated proteins and compounds derived from
the phenylpropanoid pathway (Somerville et al., 2004; Van de Wouwer et al., 2018; Lorenzo
et al., 2019; Terrett and Dupree, 2019). The architecture of the cell walls may vary according
to each cell type, however, in general, they are classified into two types: primary and secondary
(Taiz and Zeiger, 2006). Primary walls are formed while cells are still differentiating, usually
during expansion and stretching, and are generally non-lignified (Harris and Stone, 2008; Zeng
et al., 2014). They are composed mainly by polysaccharides – cellulose, hemicellulose and
pectin – and water (Figure 1 A – Loqué et al., 2015). In contrast, secondary walls are deposited
after cessed cell elongation inside the primary walls and typically present lignin in their
composition (Harris and Stone, 2008). There are two types of the secondary cell wall,
parenchyma type and sclerenchyma type (Zeng et al., 2014). The parenchyma type is present
in the parenchyma and collenchyma, which are living cells; the sclerenchyma type is thicker
and highly differentiated and is found in tracheids and fibers, which are dead cells (Zeng et al.,
2014). Although they have variable structures, the secondary walls are organized so that the
cellulose microfibrils are embedded in a complex matrix of hemicellulose, pectin, and lignin
(Figure 1 B) (Schubert, 2006; Loqué et al., 2015; Van de Wouwer et al., 2018; Lorenzo et al.,
2019; Terrett and Dupree, 2019). Due to the structural complexity and the way it chemically
binds to cellulose, lignin is the major contributor to secondary cell wall recalcitrant to
degradation (Li et al., 2008; Mottiar et al., 2016; Mahon and Mansfield, 2019).
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Figure 1. Schematic model of plant cell wall composition. (A) Primary plant cell wall found
n dicots (B) Lignified Secondary Plant cell wall. This figure is from Loqué et al., 2015.
The current world demands the production of large amounts of energy and
nowadays the main source comes from fossil representing 80% of total consumption (Seh et
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al., 2017; Correa et al., 2019). Population growth has increased the demand for energy and,
consequently, there are concerns about the depletion of oil and the environmental impacts
caused by the emission of greenhouse gases (Mccann and Buckeridge, 2014; Seh et al., 2017).
Studies indicate that by 2040 biofuel demand may increase to 4.7 mboe/d, representing 6% of
renewables will be used in transport (IEA, 2018). According to International Energy Agency,
(2018) to the world achieve the 2030 sustainable developmental scenario the biofuel production
would have to triplicate, and for this reason to implement more efficient biofuel production is
needed (Correa et al., 2019). Thus, the development of energy from renewable energy sources
to replace fossil fuels has been the priority of many countries (Himmel et al., 2007). The use of
biomass to generate energy is important for the sustainable development since this source of
energy can provide liquid fuel for transportation (Alalwan et al., 2019; Correa et al., 2019). The
production of biofuels such as biodiesel and bioethanol can contribute to supplying energy
demand in a safe way, promoting rural development and reducing the emission of noxious gases
(Macrelli et al., 2014).
Bioethanol production from biomass can be based on first-generation or second-
generation technology (Mccann and Buckeridge, 2014). The first-generation technology is
made by direct fermentation of extract obtained from plants with a high content of sucrose, such
as sugarcane and beet, or through saccharification (splitting of the starch into glucose) followed
by fermentation, such as corn and wheat (Mccann and Buckeridge, 2014). Despite the relative
success of ethanol production from sugarcane, corn, and wheat, the use of these sources of
biomass can generate competition due to its use as food for humans and animals and biodiverse
landscape (Alalwan et al., 2019; Correa et al., 2019). Second-generation biofuel production
could optimize the use of these plant resources and help to reduce the emission of polluting
gases from fossil fuel use (reviewed by Correa et al., 2019). Second-generation technology, in
turn, uses enzymatic conversion of lignocellulosic material to ethanol production (Mccann and
Buckeridge, 2014). In this case, the cell wall structural polysaccharides are broken down by
hydrolyzes or thermochemical process into fermentable monomeric sugars by microorganisms
(Alalwan et al., 2019). However, lignocellulosic biomass is underutilized because lignin
restricts the access of microbial enzymes to cellulose (Mahon and Mansfield, 2019). Besides
the negative effect on the production of biofuels, lignin in biomass also affects the efficiency
of paper industry, where lignin is undesirable because its removal increases costs (Ververis et
al., 2004; Alalwan et al., 2019; Mahon and Mansfield, 2019). Thus, it is of great economic
interest to produce plants which accumulate less lignin or contain lignin with altered
composition facilitating cellulose extraction. Several studies have been carried out in recent
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years in an attempt to understand the most varied aspects from lignin metabolism, from the
characterization of genes and enzymes involved in the biosynthesis pathway to the
identification of transcriptional regulators and enzymes related to polymerization (Wang and
Dixon, 2012; Faraji et al., 2018; Takeda et al., 2018; Pereira et al., 2018; Oyarce et al., 2019;
Ralph et al., 2019; Takeda et al., 2019; Terrett and Dupree, 2019).
Lignin is a phenylpropanoid and its route of biosynthesis shares common
intermediates with another economically important group of phenylpropanoids, chlorogenic
acids (CGA). Even though it is well known that lignin and CGAs biosynthesis pathway shares
common intermediates and enzymes, it has not been proven yet if CGAs can be used as
substrates for the biosynthesis of lignin (Joët et al., 2009; Escamilla-Treviño et al., 2014;
Valiñas et al., 2015). CGAs is commonly found in plants and some have significant amounts of
this phenol. Using the information available about each biosynthesis route, this work proposes
to investigate this superposition.
Chlorogenic acids biosynthesis
Chlorogenic acids (caffeoylquinic acids – CGAs) belong to an important group of
dietary antioxidants (Niggeweg et al., 2004; Lallemand et al., 2012a; Lallemand et al., 2012b).
These metabolites are soluble esters formed from the conjugation of trans-cinnamic acids and
quinic acid (Clifford, 1999; Lallemand et al., 2012a). CGAs have an important role in plant
defense, considering they act as antioxidants in plants. High levels of CGAs can increase
pathogen resistance (Niggeweg et al., 2004; Leiss et al., 2009; Pu et al., 2017) and prevent
damage caused by abiotic stresses (Clé et al., 2008; Comino et al., 2009). In fact, CGAs have
been described as involved in anti-herbivore compound (Leiss et al., 2009; Kundu and
Vadassery, 2019). Their pro-antioxidant effect gives to CGAs an anti-nutritive property when
consumed by insects (Kundu and Vadassery, 2019). For example, plants of chrysanthemum
(Dendranthema grandiflora) with a higher content of CGAs showed a higher level of resistance
to Frankliniella occidentalis, an important insect pest of agricultural (Leiss et al., 2009). In
sweet potato, the CGA content was associated with resistance to fungi and immature insects
(Peterson et al., 2005). Gauthier et al., (2016) associated an increase in CGA content as a
strategy to defend small-grain cereals and maize from fungi attack. In addition, its nutraceutical
value has been described against several different human conditions (Yoshimoto et al., 2002;
Islam, 2006; Thom, 2007; Yamaguchi et al., 2008; Van Dijk et al., 2009; Oboh et al., 2013;
Ohkawara et al., 2017).
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The biosynthetic pathway of CGA in plants has yet to be completely elucidated,
despite three routes have been proposed (Fig. 2) (Escamilla-Treviño et al., 2014). The first CGA
formed is 5-CQA and little is known on how the more complex chemical structures of this
chemical group are formed.
Figure 2. Proposed biochemical routes for the biosynthesis of CGAs and lignin. The
shikimate shunt is highlighted in blue and constitutes a common route towards both CGAs and
lignin. The quinate shunt towards CGAs is highlighted in red. An alternative route employing
cinnamoyl glucosides as activated intermediates are shown in green. For the biosynthesis of
diCQAs, caffeoyl quinate is first transported to the vacuole where HQT catalyzes
chlorogenate:chlorogenate transferase (CCT) activity at lower pH. The pathway from caffeoyl-
CoA towards G and S lignin units is channeled by CCoAOMT and involves other downstream
enzymes. Abbreviations: PAL, phenylalanine ammonia-lyase; C4H, cinnamate 4-
hydroxylase; C3H, p-coumarate 3-hydroxylase (ascorbate peroxidase); 4CL, 4-
coumarate:CoA ligase; HCT, hydroxycinnamoyl-CoA:shikimate hydroxycinnamoyl
transferase HQT, hydroxycinnamoyl-CoA:quinate hydroxycinnamoyl transferase; C3’H, p-
coumaroyl shikimate 3′-hydroxylase (CYP98); CSE, caffeoyl shikimate esterase;
CCoAOMT, caffeoyl-CoA 3-O-methyltransferase; UGT84, UDP-glucoside transferase;
HCGQT, hydroxycinnamoyl D-glucose:quinate hydroxycinnamoyl transferase. This figure
is from Silva et al., 2019.
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The first route described is also a branch of lignin pathway biosynthesis. In this
route the shikimate shunt, the enzyme hydroxycinnamoyl CoA: shikimate hydroxycinnamoyl
transferase (HCT) catalyzes the esterification of p-coumaroyl-CoA and shikimate in p-
coumaroyl shikimate. The 3’-hydroxylation of p-coumaroyl shikimate by p-coumaroyl
shikimate 3-hydroxylase (C3’H) produces caffeoyl shikimate, which is further converted in
caffeoyl-CoA by the activity of HCT (Hoffmann et al., 2004) or caffeoyl shikimate esterase
(CSE), with an intermediary step were caffeic acid is produced (Vanholme et al., 2013b). In
this route, until this point, lignin and CGAs would have the same pathway, and, only after
Caffeoyl-CoA production, this compound could either be channeled into the biosynthesis of
monolignols by the enzyme CCoAOMT or be converted into CQA by HCT/HQT. The second
pathway involves the conversion of p-coumaroyl-CoA into p-coumaroyl quinate through the
activity of HCT or HQT, followed by the 3’-hydroxylation by C3’H to produce 5-CQA, the
quinate shunt (Figure 2). In the last possibility, the 5-CQA is produced by a transesterification
reaction involving a caffeoyl glucoside as the activated intermediate instead of from caffeoyl-
CoA (Villegas and Kojima, 1986). It is important to highlight here the interconnection between
the shikimate and the quinate shunt by caffeoyl CoA since the conversion of caffeoyl CoA to
CGA mediated by HCT/HQT enzymes are reversible reactions (Figure 2). The fact that
HCT/HQT catalyzes more than one reaction and theses reactions are reversible, increases the
complexity between these routes and the importance of each reaction for a metabolic balance
between the pathways become a difficult task.
Although the route through HQT has been considered the main pathway to CGAs
production (Niggeweg et al., 2004) the involvement of HCT in CGAs metabolism has been
demonstrated in several species such as tobacco, Populus trichocarpa, coffee and switchgrass
(Hoffmann et al., 2003; Lallemand et al., 2012b; Escamilla-Treviño et al., 2014; Zhang et al.,
2018) even though HCT affinity to quinate is much lower than HQT (Lallemand et al., 2012b;
Walker et al., 2013). Moreover, it has been shown that not all species have HQT in their genome
(Escamilla-Treviño et al., 2014) leading to the argument that different species may have
different routes to produce CGAs. For example, an HCT was described displaying specificity
to both substrates quinate and shikimate in coffee (Lallemand et al., 2012a). For fruits from this
same species, Joët et al. (2009) suggested that HQT activity would be restricted to perisperm
while HCT would be responsible for CGAs production in the endosperm, suggesting that
different routes could operate in different tissues in other species too, i.e. the biosynthesis of
CGAs in plants might be tissue-dependent. In this sense, the routes could be regulated by
20
enzymes and substrates availability and/or cellular pH since HQT and HCT seem to have
optimal activity at different pH (Lallemand et al., 2012b).
In Panicum virgatum L.(switchgrass), biochemical characterization with a
recombinant protein of PvHCT-like showed that PvHCT-like 1a and PvHCT-like 2a have
preference to shikimic acid as acyl acceptor while the PvHCT-like 1 prefers quinic acid as acyl
acceptor indicating that this last one has HQT activity (Escamilla-Treviño et al., 2014). The
same assay showed that although PvHCT-like 1 and PvHCT-like 2 can catalyze the reverse
reaction converting caffeoyl shikimate to caffeoyl CoA, this reaction occurs very inefficiently,
and it is unlikely that it occurs in switchgrass. Besides in vitro evidence, until this moment no
PvHCT-like mutant was developed to confirm the correlation between CGA and PvHCT-like
in switchgrass. The same work also suggested the involvement of CSE in CGA pathway in
switchgrass using the branch shared with lignin biosynthesis. When Escamilla-Treviño et al.
(2014) tested stem protein extracts to produce caffeoyl CoA from caffeoyl shikimate, the main
compound produced was caffeic acid, indicating that CSE bypass HCT second step to produce
caffeoyl CoA. Valiñas et al. (2015) found a positive correlation between CSE and HQT
transcripts and 5-CQA accumulation and an opposite pattern in HCT transcripts in potato tuber.
The authors attributed the negative correlation between HCT and 5-CQA by the competition by
substrate between CSE and HCT and indicate HCT as probably involved in 5-CQA catabolism.
This way, in potato tuber the CGA content would be the product of a dynamic balance between
5-CQA production via p-coumaroyl CoA by C4H and HQT and 5-CQA catabolism by HCT
and CSE. It is noteworthy that the CSE substrate (caffeoyl shikimate) is structurally similar to
caffeoyl quinate (CQA) and, thus, it is plausible to think that CSE could use CQA as a substrate
as well. Recombinant Arabidopsis CSE also showed a broader specificity (Vanholme et al.,
2013b) suggesting that CSE might be able to use shikimate and quinate esters as substrates,
even though with different efficiencies. Moreover, CSE steps end with caffeoyl CoA formation
(see figure 2) and that HCT may convert caffeoyl-shikimate to caffeoyl CoA form, the CGA
biosynthesis through HQT activity may change according to the pool size of caffeoyl CoA. In
this way, considering that caffeoyl CoA can be used as a substrate by both, CCoAOMT in lignin
and HQT/HCT in CGA, it is tempting to think that CGA is a metabolic reservoir of caffeoyl
acid and plays a role in lignin metabolism (Días et al., 1997; Joët et al., 2009; Escamilla-Treviño
et al., 2014). Considering this hypothesis, the CSE could have a key role in CGA accumulation
for species that use caffeoyl CoA as a substrate to produce this antioxidant. But, CSE has been
discovered recently and, although it had been added in the lignin/CGA shunt (Vanholme et al.,
2013b), its metabolic role has not been completely elucidated. There are only a few studies with
21
CSE mutants (Vanholme et al. 2013; Ha et al. 2016; Saleme et al. 2017; Vargas et al., 2016)
and most of them have only evaluated its impact in lignin biosynthesis (Vanholme et al. 2013;
Ha et al. 2016; Vargas et al., 2016). For this reason, further studies are necessary to understand
CSE impact in CGA biosynthesis pathway.
Considering its complexity, the study of CGA biosynthesis goes beyond its health
benefits. Furthermore, CGA route may acts as a carbon skeleton to lignin pathway, a compound
with big impact in biofuel production (Días et al., 1997; Comino et al., 2009; Joët et al., 2009;
Escamilla-Treviño et al., 2014). It is a plausible idea, independently of which of these two main
routes are used to its biosynthesis, considering that both pathways have common intermediates
and branches to produce them. In Chapter 1 we discuss in further details this interconnection
and how the dynamic between lignin and CGA pathways possible happens in different species.
Even though there are several studies suggesting this interconnection how it happens still needs
further studies.
Lignin biosynthesis
Lignin is mainly deposited in vessels and fiber cells, enabling vascular plants to
stand upright, endure mechanical stresses, transport water in the xylem and avoid vasculature
collapse under negative pressures caused by transpiration (Ferrer et al., 2008; Bonawitz and
Chapple, 2010; Pereira et al., 2018). Lignin forms a complex linkage with cell wall
polysaccharides - cellulose, hemicellulose, and pectin – providing the plant cell wall with
recalcitrance against degradation (Lorenzo et al., 2019; Terrett and Dupree, 2019). Plant cell
wall polysaccharides can be converted to fermentable sugars to produce second-generation
biofuels (Mccann and Buckeridge, 2014; Alalwan et al., 2019), but the processing of
lignocellulosic biomass is still hampered by the presence of lignin (Mahon and Mansfield,
2019).
In most angiosperms, lignin is composed of two major monomers, the subunits
guaiacyl (G) and syringyl (S), with only trace amounts of the p-hydroxyphenyl (H) subunit
(Boerjan et al., 2003; Vanholme et al., 2019b). The ratio of the S/G subunits in lignin will
determine the degree and nature of polymeric cross-linking (Ferrer et al., 2008). The lower
methoxylation level in G subunits causes a higher rate of carbon-carbon linkages with a
consequent increase in chemical stability resulting in higher rigidity and hydrophobicity than
lignin-rich in S subunit (Ferrer et al., 2008). Radical coupling during lignin polymer deposition
is not enzymatically controlled and for this reason, lignin structure is determined by the
monomer’s availability during its polymerization (Mottiar et al., 2016; Ralph et al., 2019). For
22
this same reason, several other phenylpropanoids derivates can be naturally incorporated into
polymeric lignin (Ralph et al., 2019; Vanholme et al., 2019b). Hydroxycinnamoyl alcohols,
hydroxycinnamoyl esters, hydroxypropanols, hydroxycinnamic acids,
hydroxycinnamaldehydes, hydroxystilbenes, flavone, and tricin are some examples of these
non-canonical monomers (reviewed by Vanholme et al. 2019). Another fact that influence
directly the complex nature of lignin structure is how different interunit linkage types connect
lignin monomers (Ralph et al., 2019). In addition, how this complex polymer cross-link into
cell wall polysaccharides may also influence plant cell wall recalcitrance (Terrett and Dupree,
2019). This way, due to the high complexity of lignin polymer and its interconnection with
polysaccharides in plant cell wall some strategies has been proposed to reduce biomass
recalcitrance: A) changing lignin composition; B) changes in lignin structure; C) changes in
lignin cross-linking with plant cell wall polysaccharides (Marriott et al., 2015; Ralph et al.,
2019; Terrett and Dupree, 2019; Vanholme et al., 2019b).
There are eleven main enzymes described as involved in lignin biosynthetic
pathway and enzyme HCT is positioned at the beginning of the lignin pathway and it was first
described as essential to produce G and S subunits in tobacco (Hoffmann et al., 2003).
Hoffmann et al. (2004) first proved HCT activity in planta through gene-silencing in
Arabidopsis thaliana and Nicotiana benthamiana. Since this finding, several studies have
shown that the silencing of HCT results in altered lignin content and composition (Hoffmann
et al., 2004; Chen et al., 2006; Shadle et al., 2007; Wagner et al., 2007; Gallego-Giraldo et al.,
2014; Peng et al., 2014; Ponniah et al., 2017). In the shikimate shunt (see Figure 1) this enzyme
catalyzes the esterification of p-coumaroyl-CoA into p-coumaroyl shikimate. In another step in
this same shunt, HCT uses caffeoyl shikimate to produce caffeoyl CoA (Hoffmann et al., 2003).
However, in vitro enzyme assays showed that after caffeoyl CoA, p-coumaroyl CoA was the
second-best substrate, thus suggesting a reverse reaction (Hoffmann et al., 2003). Although the
reaction caffeoyl shikimate/quinate to caffeoyl CoA was proved in vitro, the efficiency was low
(Hoffmann et al., 2004; Lallemand et al., 2012b; Escamilla-Treviño et al., 2014; Wang et al.,
2014a).
Caffeic acid was recently described as the product of the conversion of caffeoyl
shikimate by the enzyme caffeoyl shikimate esterase (CSE) in Arabidopsis thaliana (Vanholme
et al., 2013b). CSE role in lignin pathway was already described in A. thaliana, Medicago
truncatula, dicot, Leguminosae), poplar (Populus deltoides, dicot, Salicaceae), and switchgrass
(Panicum virgatum, monocot, Poaceae) (Vanholme et al. 2013; Ha et al. 2016; Saleme et al.
2017; Vargas et al., 2016). Escamilla-Treviño et al. (2014) could not determine the formation
23
of caffeoyl CoA from caffeoyl shikimate when they used crude protein extract from
switchgrass. They found instead, caffeic acid as the main product of caffeoyl shikimate
conversion. Thus, altogether, although it still needs to be proved, at least for some species, the
conversion of caffeoyl shikimate into caffeoyl CoA by HCT may not be the preferable reaction
in vivo. It is plausible to think that CSE has an important role in the route, by-passing the
reaction catalyzed by HCT. Caffeic acid would be converted by 4CL to caffeoyl CoA
(Vanholme et al., 2013b), and CSE steps would end with caffeoyl CoA formation (see figure
1), which can be used in the monolignol route through CCoAOMT/CCR (Bonawitz and
Chapple, 2010) or CGA pathway by the action of HCT/HQT enzymes (Escamilla-Treviño et
al., 2014). Although CSE may be the preferable branch to caffeoyl CoA production it may not
be the only one, since some important species such as Brachypodium distachyon, maize,
sorghum, and sugarcane do not possess putative orthologues of this enzyme (Vicentini et al.,
2015; Ha et al., 2016).
Mutants down-expressing cse have less lignin and enrichment of H unit (Vanholme
et al., 2013b; Ha et al., 2016; Saleme et al., 2017), a phenotype usually found in hct (Chen et
al., 2006; Shadle et al., 2007; Wagner et al., 2007; Gallego-giraldo et al., 2011; Vanholme et
al., 2013a) and c3h (Franke et al., 2002a; Takeda et al., 2019) mutants. This result is in line
with the suggestion that CSE is part of the same branch in lignin biosynthesis. Thus, considering
that CSE steps end with caffeoyl CoA formation (see figure 2) and that HCT may convert
caffeoyl-shikimate to caffeoyl CoA form, the CGA biosynthesis through HQT activity may
change according to the pool size of caffeoyl CoA. It is possible that different factors may be
controlling the balance between these two metabolic alternatives, such as cellular localization
(vacuolar or cytoplasmatic), pH, and probably the concentration of CGA in the cell (Moglia et
al., 2014). In this way, considering that caffeoyl CoA can be used as a substrate by both,
CCoAOMT in lignin and HQT/HCT in CGA, it is tempting to think that CGA is a metabolic
reservoir of caffeoyl acid and plays a role in lignin metabolism (Días et al., 1997; Joët et al.,
2009; Escamilla-Treviño et al., 2014).
Relationship with lignin and chlorogenic acids biosynthetic pathways
Although the lignin route has been extensively studied, it is noteworthy that remains
unclear if chlorogenic acids (monocaffeoylquinic acids – CGAs) can be used as a carbon
skeleton to the lignin pathway. Several reports suggest that wounding stress in potato and
carrots can induce the conversion of CGA into lignin, probably as a defense against pathogens
(Gamborg, 1966; Friend et al., 1973; Becerra-Moreno et al., 2015; Jacobo-Velázquez et al.,
24
2015). Joët et al. (2009) found a match between the time window of gene expression pattern
related to CGA catabolism and cell wall lignification in Coffea arabica, suggesting that this
conversion would be mediated by CaHCT1. In agreement with these findings, Lepelley et al.
(2007) observed that although CGA level maintains constant during grain development, quinic
acid (CGA precursor) level falls during endosperm expansion and grain maturation in coffee,
the same period when there is an increase in HCT and CCoAOMT gene expression. A decrease
of quinic acid (Rogers et al., 1999) and an increase in total CGA (Bertrand et al., 2003; Castro
and Marraccini, 2006) content during coffee grain development had been previously reported.
Also, in coffee, a decrease in CGAs coincides with an increase in lignin deposition in seedlings
(Aerts and Baumann, 1994). Similar results were described by Días et al. (1997) in Capsicum
annuum L. during plant early development. Thus, it might be possible that the balance between
the two routes changes depending on the cell types and the cell need for carbon allocation in
new structures. For example, HQT role might be punctual depending on the plant development
stage or environmental conditions.
The transcriptional profile developed by Joët et al. (2009) also suggests the
remobilization of CGA toward monolignols biosynthesis in coffee seeds, as the first three
enzymes of phenylpropanoid pathway (PAL, C4H, and 4CL) did not display a gene
transcription profile that matched with lignification process associated with endosperm
hardening during coffee seed development. Interestingly, Mondolot et al. (2006) reported in C.
canephora the transportation of CGA to vascular tissue in old leaves indicating its
remobilization among tissues. In coffee, most chlorogenic acid studies are in endosperm
(Rogers et al., 1999; Castro and Marraccini, 2006; Lepelley et al., 2007; Joët et al., 2009) due
the fact that these compounds have an important role in drink quality (Clifford, 1999;
Mazzafera, 1999; Casas et al., 2017). Despite its importance to drink quality, the coffee seed is
physiologically very different from the stem – the main source of lignocellulosic biomass.
Trees and grasses are the main biomass reservoir and the focus in most studies that try to
understand lignin biosynthesis. For example, maize and sugarcane have been gaining the
attention of scientists that study lignin due to its potential to improve biofuel production (Jung
et al., 2012; Vicentini et al., 2015; Fornalé et al., 2017). Thus, understanding the relationship
between HCT, CSE, and HQT and how they can influence lignin and CGA pathways can be
useful for the development of plants with lower lignin content and highest antioxidant potential
due to CGAs accumulation. On the one hand, studies in several species such as tobacco, coffee,
sorghum, poplar and switchgrass (Hoffmann et al., 2004; Lallemand et al., 2012b; Walker et
al., 2013; Escamilla-Treviño et al., 2014; Wang et al., 2014a) suggest that HCT can act in both
25
lignin and CGA pathways. On the other hand, HCT has lower in vitro affinity to quinate
(Lallemand et al., 2012b) than HQT, which is described as the main enzyme responsible for
CGA biosynthesis in coffee, artichoke, potato, tomato and tobacco (Niggeweg et al., 2004;
Lepelley et al., 2007; Comino et al., 2009; Sonnante et al., 2010; Payyavula et al., 2015). In
addition, only a few studies have explored the potential role of CCoAOMT in CGA
accumulation (Campa et al., 2003; Jiang et al., 2014; Valiñas et al., 2015), despite its function
in stress response (Senthil-Kumar et al., 2010; Giordano et al., 2016; Wang and Balint-Kurti,
2016). Noteworthy, Valiñas et al., (2015) found a strong correlation between CCoAOMT
expression and CGA production in potato. Similarly, in coffee, a CCoAOMT allele seems to
be strongly related to CGA production (Campa et al., 2003). The correlation between CGAs
and lignin during early plant development was studied in Capsicum annuum L. (Días et al.,
1997). The authors suggested that CGAs were potential precursors for lignin biosynthesis, a
hypothesis later endorsed by the work of Joët et al., (2009) in coffee. Considering that caffeoyl-
CoA can be used as a substrate by both CCoAOMT in lignin and HQT/HCT in CGA
biosynthesis, it is plausible to think that CCoAOMT regulation may affect CGA content. There
is a possibility that CGA is a storage compound that is subsequently re-routed towards lignin
biosynthesis during specific developmental stages (Días et al., 1997).
This thesis – efforts to get stronger evidence that CGA is substrate for lignin
biosynthesis
In order to gain insights into the correlation between the two routes, we developed
transgenic tobacco downregulated for three key enzymes (CSE, HQT, and HCT), including
lines in which the overexpression of one gene is followed by the repression of another. Briefly,
we transformed plants with seven different constructs – pCaMV35S::CSE (CSE
overexpression), pCaMV35S::HCT (HCT overexpression); pCaMV35S::HQT (HQT
overexpression); pCaMV35S::amiRNACSE (CSE downregulation),
pCaMV35S::HCT::pCsVMV::amiRNAHQT (HCT overexpression combined with HQT
downregulation); pCaMV35S::HQT::pCsVMV::amiRNAHCT (HQT overexpression
combined with HCT downregulation), and pCaMV35S::HCT::pCsVMV::amiRNACSE (HCT
overexpression combined with CSE downregulation). Although we have developed transgenic
lines for all seven constructs, due to technical problems, not all were analyzed. We had powdery
mildew contamination in our greenhouse that destroyed half of our plants before flowering.
Moreover, our double mutants pCaMV35S::HCT::pCsVMV::amiRNAHQT (HCT
overexpression combined with HQT downregulation);
26
pCaMV35S::HQT::pCsVMV::amiRNAHCT (HQT overexpression combined with HCT
downregulation) generated several lines (10 and 80 respectively) but we did not find in T0 any
line with high level of expression of the gene we were overexpressing combined with the
downregulation of the gene we were downregulating. When we screened the plants transformed
with pCaMV35S::HCT (HCT overexpression); pCaMV35S::HQT (HQT overexpression) we
were able to find only one line of each construct with a high level of expression of the gene we
were expressing. For this reason, we did not use transformed plants with these five constructs
in Chapter 2, although the construction of the vectors and the production of the plant mutants
took a considerable time. In Chapter 3, we applied genome editing by CRISPR/Cas9 to induce
mutations in tobacco by agro-transient expression in CSE, CCoAOMT, and HCT. We also
developed 8 stable plants containing the construction CRISPR HCT but we did not find any
mutation what may be related with the low number of plants evaluated. Genome editing by
CRISPR/Cas9 allows the development of more reproducible and accurate results, besides the
generation of “transgene-free” plants, which would imply in higher acceptance by the general
public (Belhaj et al., 2015; Lowder et al., 2015; Ma et al., 2015; Tong et al., 2015; Zhou et al.,
2015). In Chapter 3 we reviewed and discuss biochemical and molecular evidence of the
metabolic re-routing of CGAs towards lignin. Most of the studies regarding CGA and lignin
relationship evaluated the carbon flow between these pathways only in vitro by enzymatic assay
or by the analysis of the transcript level. Although these approaches are important and helpful,
it is only the first steps towards understanding CGA and lignin metabolic relationship. Studies
using mutants for different genes of both pathways with a metabolomic approach would help
to clarify the role of CGA in lignin and how it works in vivo. This information can be used to
improve biomass utilization for second-generation bioethanol and cellulose production, but also
to improve food quality, since high levels of antioxidants such as CGAs have high nutraceutical
value.
27
Chapter 1 This Chapter was published as an article review at Phytochemistry
Volpi e Silva N, Mazzafera P, Cesarino I. Should I stay or should I go: are chlorogenic
acid mobilized towards lignin biosynthesis? V 166, October 2019, 112063, DOI:
https://doi.org/10.1016/j.phytochem.2019.112063
28
Should I stay or should I go: are chlorogenic acids mobilized towards lignin biosynthesis?
Nathalia Volpi e Silvaa, Paulo Mazzaferaa,b, Igor Cesarinoc,*
a Department of Plant Biology, Institute of Biology, State University of Campinas, Campinas-
SP, Brazil
b Department of Crop Science, College of Agriculture “Luiz de Queiroz”, University of São
Paulo, Piracicaba - SP, Brazil
c Department of Botany, Institute of Biosciences, University of São Paulo, Rua do Matão 277,
CEP 05508-090, São Paulo - SP, Brazil
*Corresponding author: Igor Cesarino, +55 11 3091 7550, [email protected]
Abstract
Chlorogenic acids (CGAs) and the biopolymer lignin are both products of the
phenylpropanoid pathway. Whereas CGAs have been reported to play a role during stress
responses, lignin is a major component of secondary cell walls, providing physical strength and
hydrophobicity to supportive and water-conducting tissues. Because the chemical structure of
CGAs largely resembles those of some lignin intermediates and because CGAs can be
converted back to hydroxycinnamoyl-CoAs in vitro, CGAs have been considered authentic
intermediates of the lignin biosynthetic pathway. However, it is still unclear whether and how
the CGA pool can be channelled towards the production of lignin monomers in response to
developmental or environmental signals. Comprehensive studies on the catalytic activity of
recombinant enzymes together with functional characterizations in planta have been very useful
in understanding the potential interdependence between these two metabolic routes. Here we
present the current understanding on CGA metabolism and discuss the biochemical and
molecular evidence of the metabolic re-routing of CGAs towards lignin.
Key words: chlorogenic acids; lignin; phenylpropanoids; shikimate; quinate;
hydroxycinnamoyl CoA:shikimate/quinate hydroxycinnamoyl transferase; caffeoyl shikimate
esterase
Chapter 1 - The full version of this article is available online at the link:
https://www.sciencedirect.com/science/article/pii/S0031942219304868
29
Chapter 2
30
The role of CSE and HCT in chlorogenic acid and lignin biosynthesis in tobacco.
Nathalia Volpi e Silvaa; Felipe Thadeu Tolentinoa; Ewerton Ribeiroa; Rafaela Gagetti
Bulgarellia; Juliana Mayera; Franklin Magnum de Oliveira Silvaa; Eduardo Kiyotaa; Juan P.P.
Llerenaa; Igor Cesarinoc; Paulo Mazzaferaa,b.
a Department of Plant Biology, Institute of Biology, State University of Campinas, Campinas-
SP, Brazil
b Department of Crop Science, College of Agriculture “Luiz de Queiroz”, University of São
Paulo, Piracicaba - SP, Brazil
c Department of Botany, Institute of Biosciences, University of São Paulo, Rua do Matão 277,
CEP 05508-090, São Paulo - SP, Brazil
Abstract
Phenylpropanoids are involved in several aspects related to the defense of biotic and abiotic
stresses. Chlorogenic acids (CGA) and the lignin are both part phenylpropanoid pathway. While
CGAs have been related with tolerance to stresses and diseases, lignin is a major component of
secondary cell walls, providing physical strength and hydrophobicity to supportive and water-
conducting tissues. Although the metabolic pathway leading to the biosynthesis of CGAs and
lignin have several common intermediates, it is still unclear whether and how the CGA pool
can be used as a reservoir to lignin biosynthesis. Here we developed transgenic plants to HCT
and CSE genes, key genes from lignin metabolism, in order to understand the interconnection
between these metabolic pathways. We focused on the role of the enzymes CSE and HCT to
understand the interconnection between both pathways and the evaluate the impact of CGA as
carbon skeleton to lignin pathway in tobacco. Our results indicate that alteration in lignin
pathway affect CGA and plant cell wall content, especially in mutants overexpressing CSE that
showed a significant increase in CGA indicating that this gene might be also related to CGA
metabolism. In addition, a complex regulatory network among lignin biosynthesis seems to be
affected since other phenylpropanoid metabolites were also affected. In conclusion, our results
come to give strength to the hypothesis that CGA provides carbon skeleton to the lignin
pathway. In the opposite direction, an excess of lignin biosynthesis may redirect carbon to CGA
pathway.
1. Introduction
Chlorogenic acids (CGAs) belong to an important group of dietary antioxidants
(Niggeweg et al., 2004; Lallemand et al., 2012a; Lallemand et al., 2012b). These metabolites
are soluble esters formed from the conjugation of trans-cinnamic acids and quinic acid
(Clifford, 1999; Lallemand et al., 2012a). High levels of CGA can increase pathogen resistance
31
(Niggeweg et al., 2004; Leiss et al., 2009; Pu et al., 2017) and prevent damage caused by abiotic
stresses (Clé et al., 2008; Comino et al., 2009). In addition, CGAs have great potential as
nutraceuticals considering its benefits for the human health, mainly as antioxidants (Olthof et
al., 2001; Thom, 2007; Yamamoto and Obokata, 2008; Van Dijk et al., 2009; Oboh et al., 2013).
Lignin and CGA biosynthetic pathways potentially share intermediates and
enzymes and several studies suggests the connection between these routes (Hoffmann et al.,
2004; Lepelley et al., 2007; Joët et al., 2009; Escamilla-Treviño et al., 2014; Becerra-Moreno
et al., 2015; Jacobo-Velázquez et al., 2015). Lignin is mainly deposited in the xylem cells and
fibers rays, enabling vascular plants to stand upright, endure mechanical stresses, transport
water in the xylem and avoid xylem collapsing under negative pressures during high
transpiration rates (Ferrer et al., 2008; Bonawitz and Chapple, 2010; Pereira et al., 2018). On
the other hand, lignin forms a complex matrix with the cell wall polysaccharides - cellulose,
hemicellulose, and pectin – and is a major contributor to biomass recalcitrance (Li et al., 2008;
Cesarino et al., 2012; Mottiar et al., 2016; Lorenzo et al., 2019). This recalcitrance has
consequences in downstream applications such as chemical pulping, forage digestibility and
production of biofuels paper industry since its removal demands chemical reagents increasing
the process costs (Ververis et al., 2004; Schubert, 2006; Faraji et al., 2018; Figueiredo et al.,
2019). However, manipulation of lignin biosynthesis has provided a basis for generating plants
with reduced lignin content and altered composition, and increased saccharification efficiency
(Hoffmann et al., 2004; Vanholme et al., 2013b; Tong et al., 2015; Vargas et al., 2016; Saleme
et al., 2017).
The key enzymes linking lignin and CGA metabolism are hydroxycinnamoyl CoA:
shikimate hydroxycinnamoyl transferase (HCT), hydroxycinnamoyl CoA: quinate
hydroxycinnamoyl transferase (HQT) and caffeoyl shikimate esterase (CSE). Together, these
enzymes are responsible for the balance among caffeoyl CoA, caffeoylquinic acid (CGA) and
caffeoyl shikimic acid (Hoffmann et al., 2003; Niggeweg et al., 2004; Vanholme et al., 2013b).
At this point in the route, caffeoyl CoA can be used by the enzyme caffeoyl CoA 3-O-
methyltransferase (CCoAOMT) to produce the coniferyl and sinapyl alcohols, which are the
precursors of the G and S units in the lignin backbone, respectively, or by HCT/HQT to produce
CGA. Theoretically, an excess of CGA could be converted to caffeoyl CoA to be used for lignin
production (Joët et al., 2009; Escamilla-Treviño et al., 2014). On the other hand, an over
stimulate lignin biosynthesis could re-direct precursors to CGA biosynthesis.
32
So far, most of the data supporting this interconnection were obtained from in vitro
or transient assays (Hoffmann et al., 2004; Joët et al., 2009; Escamilla-Treviño et al., 2014;
Valiñas et al., 2015). Here, we used genetically modified tobacco plants to provoke an
unbalance between CGA and lignin pathways to obtain evidence of their interconnection. We
produced simple and double mutants to the enzymes HCT and CSE. It is already accepted that
there is an interconnection between CGA and lignin (Hoffmann et al., 2004; Lepelley et al.,
2007; Joët et al., 2009; Escamilla-Treviño et al., 2014; Becerra-Moreno et al., 2015; Jacobo-
Velázquez et al., 2015; Valiñas et al., 2015), but the intensity it happens and how this can affect
mainly lignin biosynthesis is still unknown. Here we aimed to study the interconnection
between lignin and CGA through Caffeoyl CoA and the role of CGA as carbon skeleton to
lignin biosynthesis. Moreover, CSE was recently discovered in lignin pathway (Vanholme et
al., 2013b) and before that, it was believed that HCT was responsible to convert caffeoyl
shikimate into caffeoyl CoA, even though in vitro reactions showed that HCT is more efficient
in the conversion of the reverse reaction (Hoffmann et al., 2004; Lallemand et al., 2012b; Wang
et al., 2014a). The role of HCT in caffeoyl CoA production has only been proved in an
enzymatic assay and no one has ever proved if HCT is able to convert caffeoyl shikimate into
caffeoyl CoA in plant. Here, we show that caffeoyl CoA is produced mainly via CSE in tobacco.
Furthermore, we obtained strong evidences that HCT is capable to produce caffeoyl CoA in
vivo in the absence of CSE and recover normal lignin and CGA biosynthesis.
2. Material and Methods
2.1. Sequence analysis
Search for the full length sequences of all alleles from HCT, HQT and CSE through
search by keywords and alignment with sequences previously described in the literature
[Hoffmann et al., (2003) (AJ5078251), Niggeweg et al., (2004) (AJ582651 e AJ582652) and
Vanholme et al., (2013b) (AT1G52760)] in public database available: NCBI (National Center
for Biotechnology Information), SOL Genomics (Bombarely et al., 2011) and Tabacco EST
clones from BY-2 cells Database Search (Altschul et al., 1997). Tobacco was chosen as plant
material for this study because it accumulates chlorogenic acids (Niggeweg et al., 2004). The
sequences were aligned in BioEdit Sequence Alignment Editor v. 7.0.9.0 (Hall, 1990) using the
following parameters: 85% minimum match percentage and 20 bp minimum overlap. The
contigs obtained were confronted with sequences from NCBI using the algorithm BlastX
33
(Altschup et al., 1990). Domain search was performed using ScanProsite
(http://www.expasy.ch – Castro et al., (2006).
2.2. Vector assemble and amiRNA design
RNA was extracted from tobacco leaves based on Chang et al., (1993) protocol.
DNAse I from Bio-Rad™ was used to eliminate DNA and quantification was made at 260 nm
in a spectrophotometer. cDNA synthesis was performed with Superscript III ®(Invitrogen™).
Full-length HCT and partial CSE (877 from 984 bp) were amplified by RT-PCR and the product
of amplification was cloned in pDONR221® vector using Gateway® System. The vectors used
to create the cassette of expression are based in Gateway® technology this way, two rounds of
PCR were required to insert the attB recombination site into the PCR product. The first PCR
consists of the amplification of the gene of interest and partial addition of the attB site. The
second round of PCR completes the addition of the full attB site, which allows recombination
of the PCR product with the pDONR221, the entry vector. The PCR product was used for
recombination between the entry vector (pDONR221 ™) and the insert through the BP clone
enzyme. After confirming the identity of the cloned genes in pDONR221 ™, the LR clonase
reaction from the Gateway® system was performed for recombination of the genes with the
final expression vector pK7GW2.
To design the amiRNA primers to CSE gene we used the website
http://wmd3.weigelworld.org/cgi-bin/webapp.cgi (Ossowski et al., 2008). The amiRNA was
designed using MIR319a as a precursor by PCR-based mutagenesis and the plasmid pRS300
(Schwab et al., 2006). The first step in the construction of the amiRNAs consists of 3 PCRs:
(a), (b) and (c). The second step is to use the three reactions of the first step ((a), (b), (c)) as the
template for amiRNA production. After this step, we added the attB site by PCR using the
primers AttB1 and AttB2. The PCR product was used to perform the Gateway recombination
using BP clonase reaction with the pDONR221 ™ entry vector and the colonies obtained were
submitted to colony PCR to confirm the presence of amiRNAs in the vector. After confirmation
in the pDONR221 ™ vector, a colony was sequenced to confirm it the correct sequence was
inserted in the entry vector. Next, it was possible to proceed through the assembly of the
amiRNACSE to the final vector pK7GW2 by LR clonase reaction. The insertion of the amiRNA
into the pK7GW2 vector was confirmed by PCR. Moreover, the vector was transformed into A.
tumenfaciens and colonies that were positive in colony PCR were used for the transformation
of tobacco.
34
In total, three different constructions were developed. First, we assembled by
Gateway® the amiRNA from CSE with the constitutive promoter CsVMV (Verdaguer et al.,
1996) into pK7WG2 vector, to create the downregulation vector. Second, we produced two
vectors, the overexpression vectors for HCT and CSE both using CaMV35S promoter in
pK7WG2. Third, we produced a double cassette construction using the cassette from Gateway®
Multisite pXB2m43GW2, to insert pCsVMV::amiCSE, and fuse it to pK7WG-HCT. After
cloning HCT into pK7WG2, we digested it with XbaI for linearization and treated with alkaline
phosphatase for dephosphorylation. The same enzyme was used to remove m43GW2 fragment
from pXB2m43GW2. After purification of the fragments, the DNA was quantitated using
Nanodrop and pK7GW2-HCT and the m43GW2 fragment was fused by T4 ligase from
Invitrogen, following manufacturer's guidelines. The engineered vector was cloned into E.coli
resistant to ccdb. Using this strategy, a new site of recombination by Gateway Multisite® was
created: pK7WG2-HCT::m43GW2. We used three building blocks to assemble this new
recombination site and generate a double cassette vector: to insert pCsVMV, we used pEN-L4-
4-R1, insert amiCSE we used pDONR221 and to insert the terminator tOCS we used pEN-R2-
8-L3. After recombination, the vectors were cloned in E.coli DH10B by thermal shock and
colony PCR was done to confirm if the construct was correct. After confirmation, the vectors
were inserted into A. tumenfaciens and the positive colonies were used for tobacco
transformation.
2.3. Plant Transformation
In order to generate stable tobacco plants, the binary vectors were introduced into
Agrobacterium tumenfaciens EHA105 and leaf tissues were transformed following the protocol
described by Horsch et al., (1985). We used 3 months old tobacco plants. Six rounds of
transformation were performed, in each of the experiments. Each transformation experiment
referred to the transformation of a specific construct described in Table 1. The plants that
showed normal roots development were transferred to the growth chamber under a photoperiod
of 12 h of light and 25°C. T1 plants were germinated in vitro in a B.O.D. chamber set to 16 h
of light and 25°C in MS medium (Murashige and Skoog, 1962), supplemented with Kanamycin
(100 mg / L) and transferred to greenhouse after one month.
35
Table 1. Experiment design to obtain transgenic plants.
Construction
used
Expected Result Number of Plants
obtained
CSE 1 e 2 Overexpression of CSE 18
amiCSE Downregulation of CSE 9
HCT:amiCSE Overexpression of HCT and
downregulation of CSE
28
2.4. qRT-PCR analyses
Six-months-old plants cultivated in pots with a capacity of 5 kg soil in a greenhouse
were used to carry the next steps of the experiments. To analyse the level of expression of CSE,
HCT and HQT in different tissues of wild type (WT) tobacco we extracted the RNA from the
following tissues: flowers, old leaves (2 basal leaves), young leaves (4 apical leaves), old stem
(10 cm from the basal stem), young stem (10cm from the apical stem) and root, all in four
biological replicates. The samples were immediately frozen in liquid nitrogen and stored at -80
ºC until further analysis. For qPCR analyses from T1 generation from mutants, 1-year-old
plants were collected. The stem was collected excluding the first 5 cm from the base up to the
7th internode and all leaves from 7th – 10th internode were collected and stored in -80°C for
subsequent analyses. For RNA extraction we used Trizol (Life Technologies) and DNase I from
Ambion. For cDNA synthesis, we used 1 µg of RNA and the iScript cDNA Synthesis Kit (Bio-
Rad). The cDNA was diluted 50X and 3 µl were used for each qPCR reaction, which was carried
out with the iTaq Universal SYBR Green Supermix (Bio-Rad). To screen and analyze mutants
and double-mutants we extracted RNA from leaves and stem using RNAeasy Plant Mini Kit
(Qiagen) and RNase-free DNase Set (Qiagen). cDNA was produced from 500 ng of RNA using
the iScript cDNA Synthesis Kit (Bio-Rad). The cDNA was diluted 25X and 3 µl were used for
each qPCR reaction. All the analyses were done using qbase+ software, version 3.0 (Biogazelle,
Zwijnaarde, Belgium - www.qbaseplus.com). The expression level was normalized with the
constitutive genes PP2a and EF1a (Schmidt and Delaney, 2010). The primers used are shown
in Table 2.
36
Table 2. Primers designed to qRT-PCR analyses.
Primer Sequence 5’ - 3’ HQTqPCR1_F CAGATTTTGGATGGGGAAGG
HQTqPCR1_R GCCAAACGCAAGTTCCTATC
HCTqPCR_F AAACCAGCGTGTCCATCTTC
HCTqPCR_R ACACATGTCCTGCCAACATC
CSEqPCR_F TTGCCAGGCAGTGTGAATAC
CSEqPCR_R GCTTGAGGGATTTGTCCTTG
2.5. Phenolic profiling
Phenolic profiling was carried out by Ultra-Performance Liquid Chromatography
coupled to a Mass Spectrometer (UPLC-MS / MS) using the protocol described by Torras-
claveria et al., (2012) with modifications. Samples from leaf and stem (30 mg) from 1-year-old
mutants and WT plants were lyophilized and used for analyses. The stem was collected
excluding the first 5 cm from the base up to the 7th internode and all leaves from 7th – 10th
internode were collected and stored in -80°C for all biochemical analyses. The material was
extracted with 500 µL methanol:H2O (4:1, v/v). This mixture was then vortexed for 30 s,
sonicated for 10 min, and shaken for 2 h at room temperature. After centrifugation at 10.000
rpm for 15 min, the supernatant was collected and dried using a centrifugal vacuum evaporator.
The same volume of methanol:H2O (4:1, v/v) was added to the pellet and the procedure was
repeated. Dried N. tabacum extracts were made up in 600 µL ethanol:H2O (1:1, v/v) and filtered
through a 0.2 µm syringe filter (PTFE Millex-LG, Merck).
2.6. Lignin quantification
Lignin quantification was made using the acetyl bromide method (Foster et al.,
2010). We also quantified lignin monomers and S/G ratio using Ultra-Performance Liquid
Chromatography Coupled to a Mass Spectrometer (UPLC-MS) according to the protocol
described by (Mokochinski et al., 2015). After lyophilization of the samples from stem, 80mg
of each sample was used to determine S/G ratio. Initially, the samples were hydrolysed in 2mL
of 4M NaOH at 95° for 24 hours. Next, the samples were acidified with 1.6 mL of 6M HCl,
mixed for neutralization and centrifuged by 13.000 rpm for 5 minutes. An aliquot of 500 µL
from supernatant was transferred to a 2mL tube and 1mL of ethyl acetate was added to extract
the organic phase. The last step was repeated, and the samples dried under a stream of N2.
37
Lastly, the sample was resuspended into 1mL of miliQ water and the solution was analyzed in
UPLC-MS.
2.7. Plant cell wall polysaccharides and saccharification
Stem of the plants was lyophilized and used (100 mg) to determine cell wall
polysaccharides, following the protocol described by (Chen et al., 2002). The percentage of
saccharification was determined using 30 mg of lyophilized tissue sample and followed the
protocol described by (Llerena et al., 2019).
2.8. Morphological and histochemical analyses
The transgenic lines selected (three for each: HCTamiCSE and CSE) together with
WT were grown randomly in the greenhouse for 1 year when they had approximately 60-70 cm
in height (Supplementary. Figure 1). Height, internode length, leaf number and area, leaf and
stem fresh and dry weight were measured in these plants, but the number of replicates varied
for each line according to the plants available for analysis. Histochemical analyses were
performed as a first step to verify changes in lignin deposition and morpho-anatomical
alterations. Such analyses were done according to a protocol described by Vanholme et al.,
(2013b). The cuts were made in the 7th internode of the plants. For each lineage, three plants
were analyzed in order to ensure that the differences found are due to the presence of the
transgene and not just biological variations. The fresh material was stained with the Wiesner
stain (1g of phloroglucinol in 100 mL of 95% EtOH and 16 mL of 37% HCl), placing a drop
on top of the cut. For Maule staining, the samples were prepared by incubating for 5 minutes in
1% KMnO4 solution, followed by rinsing with water and incubating in 37% HCl and adding
one drop of NH4OH (14.8M). The materials were observed in a Zeiss microscope, model
AXIOSKOPE, for documentation and later analysis.
2.9. Statistical analyses
The statistical analyses were carried out with RBio (Bhering, 2017) and R statistical
software version 3.1.2 (Team, 2011). We performed one-way analysis of variance (ANOVA),
and the means were tested by the Tukey test at a 5% significance level. In order to integrate
data, we performed multivariate analysis by Principal Component Analysis (PCA) with
Minitab® 17 (Minitab 17 Statistical Software). For PCA, data were normalized to maximize the
variance of each component. Moreover, to give the results an easy understanding, a graphical
38
representation of the metabolic profiling data was provided as a heatmap (Howe et al., 2010)
and correlation network performed using RBio Software.
3. Results
3.1. Bioinformatic analysis
3.1.1. HCT
Four HCT genes (gene_27881, gene_45849, gene_29243, and gene_83292) were
identified using NCBI and SOL Genomics databases. These four genes were described in the
SOL genomics database and will be referred to in this work as HCT1, HCT2, HCT3, and HCT4
respectively. HCT1 was previously described by Hoffmann et al., (2003). In order to confirm
these data, the sequences found in BlastN were aligned to confirm the presence of 4 putative
isoforms. We used the ORF Finder (NCBI) to obtain the coding sequence (CDS) and they were
translated, aligned with each other and an identity matrix was made using the BioEdit program
(Supplementary Table 1). In order to verify the presence of the isoforms in the tobacco genome,
a BlastN was carried out in the SOL Genomics using the N. tabacum TN90 Genome database
(Figure 1). The presence of four HCT genes in the tobacco genome was confirmed at the
positions described in Supplementary Table 2.
Figure 1. BlastN in SOL Genomics from HCT in the N. tabacum TN90 Genome database
to identify tobacco haplotypes.
The search for conserved domains was performed using the Batch CD-Search tool
(Marchler-Bauer and Bryant, 2004) in the NCBI database, which allows searching of conserved
domains in multiple protein sequences. The search was done separately with the four putative
isoforms of HCT. The first domain found PLNO2663 is a nonspecific domain and is described
as domain hydroxycinnamoyl-CoA:shikimate/quinate hydroxycinnamoyltransferase.
39
Moreover, a transferase domain (pfam02458) was also found (Yang et al., 1997), this domain
is characteristic of the BAHD family of plant CoA-dependent and is also present in enzymes
anthranilate N-hydroxycinnamoyl/benzoyltransferase (HCBT) involved in the biosynthesis of
phytoalexins (Yang et al., 1997; St-Pierre and Luca, 2000), and the enzyme deacetylvindoline
4-O-acetyltransferase (DAT) (EC: 2.3.1.107), which catalyses the last step in the vindoline
pathway (St-Pierre et al., 1998). The HXXXD motif is probably the active site and characterizes
the BAHD superfamily, to which belong the enzymes HCBT, DAT, HCT and HQT (St-Pierre
and Luca, 2000).
3.1.2. CSE
To find the homologous sequence from tobacco CSE we used the protein sequence
from Arabidopsis thaliana CSE, previously described by Vanholme et al., (2013b), obtained
from TAIR database (AT1G52760). Through the analyses in the NCBI and SOL Genomics
databases, it was possible to identify the presence of two possible CSE gene haplotypes
(mRNA_119258_cds and mRNA_108581_cds – Supplementary Table 3). Using the ORF
Finder program (NCBI) the CDSs sequences were obtained, which were compared to each other
using the BioEdit program. The translated protein sequences were used to construct an identity
matrix in the BioEdit program to verify their proximity, which showed 0.965 identities between
the haplotypes. In order to confirm if there are two isoforms of CSE in tobacco, a BlastN was
performed in SOL Genomics in Genomic database (N. tabacum TN90 Genome) and two
separate loci were identified for each sequence as shown in Figure 2 and Supplementary Table
4.
Figure 2. BlastN in SOL Genomics from CSE in the N. tabacum TN90 Genome database
to identify tobacco haplotypes.
Caffeoyl shikimate esterase or lysophospholipase 2 (CSE - At1g52760) was first
functionally described in the phospholipid repair during stress conditions (Gao et al., 2010).
This enzyme was described as having monoacylglycerol O-acyltransferase, monoacylglycerol
40
lipase and lysophospholipase activities in vitro (Gao et al., 2010; Vijayaraj et al., 2012). Here,
we searched for conserved domains using the Batch CD-Search tool (Marchler-Bauer and
Bryant, 2004) in the NCBI database. The same domains were found for the two putative CSE
haplotypes we found in tobacco. We found the hydrolase_4 domain (Pfam 12146), part of the
Esterase-lipase superfamily (cl21494), and the multidomain PLN02298 and
lysophospholipases and alpha/beta hydrolases, PidB (Whayeb et al., 1996; Karlsson et al., 1997;
Nardini and Dijkstra, 1999). Other multidomain found were: PHA02857, described as
monoglyceride lipase (Esteban and Buller, 2005); Abhidrolase_6 (pfam12697), from the
alpha/beta hydrolases family; the PST-A (TIGR01607), found in Plasmodium falciparum and
Plasmodium yoelli and which is closely related to the lysophospholipases and alpha/beta
hydrolases of plants; and the multidomain PRK14875, described as the E2 subunit domain of
acetoin dehydrogenase.
3.2. Vector assembly and amiRNA design
The Sequences found (described in topic 3.1.) were used to design specific primers
(Table 3) in order to clone the genes HCT1, CSE1 and CSE2. The HCT1 was selected instead
of the other haplotypes since it has already been characterized in the literature and has its
function in the lignin pathway proved in vitro (Hoffmann et al., 2003; Hoffmann et al., 2004).
Two rounds of PCR were performed in order to clone CSE and HCT into pDONR221. The first
PCR can be seen in Figures 3A and 3Band the second round of PCR can be seen in Figure 3C.
As the first time PCR was done to add the attB overhang in CSE2 sequence had little
amplification (Figure 3C), the PCR reaction was performed again for this gene (Figure 3D).
Table 3. Primers designed to clone HCT1, CSE1, and CSE2.
Primer Sequence 5’ – 3’ AttB1 5'GGGGACAAGTTTGTACAAAAAAGCAGGCT3'
AttB2 5'GGGGACCACTTTGTACAAGAAAGCTGGGT3'
HCTTabGtw_R 5'AGAAAGCTGGGTCTCAAAAGTCATACAAGAACTTCT
C3'
HCTTabGtw_F 5'AAAAAGCAGGCTTCATGAAGATCGAGGTGAAAGA3'
CSETab2_RGtw 5'AGAAAGCTGGGTCTCAACGAGTGATACATTCCATC3'
CSETab2_FGtw 5'AAAAAGCAGGCTTCATGGCGTCCGACGTACC3'
CSETab1Gtw_F 5'AAAAAGCAGGCTTCATGGCGTCAGACGTGCC3'
CSETab1Gtw_R
5'AGAAAGCTGGGTCATGATACATTCCATCATAAAGCT
TGA3'
41
Figure 3. PCR amplifying full-length genes HCT1; CSE1 and CSE2. A) First round -
Gradient PCR HCT1, and CSE1 M – 1Kb Ladder; 1 – Negative Control 62°C; 2 – HCT1 62°C;
3 – Negative Control; 4 – HCT1 66°C; 5 – Negative Control 68°C; 6 – HCT1 68°C; 13 –
Negative Control 62°C; 14 – CSE1 62°C; 15 – Negative Control 66°C; 16 – CSE1 66°C;17 –
Negative Control 68°C; 18 – CSE1 68°C. B) First round PCR CSE2 PCR M – 100pb Ladder;
1- Negative Control; 2 – CSE2. C) Second Round PCR -Adding attB overhang M – 100 pb
Ladder; 1 – HCT1; 3 – CSE1; 4 – CSE2. D) Second Round PCR – Adding attB overhang M –
100 pb Ladder; 1 – Negative Control; 2 – CSE2.
Figure 4. Colony PCR to confirm gene insertion into pDONR221™ vector. A) HCT1 (1-
6), M -1Kb Ladder; B) CSE1 (4-6), M -1Kb Ladder; C) CSE2 (2-14), 1 – Negative Control, M-
100 pb Ladder.
To confirm the insertion of CSE and HCT into pDONR221 ™ vectors, the positive
colonies (Figure 4 A-C) were sequenced by the Sanger methodology to verify the insertion and
identify the presence of CSE1, CSE2, and HCT1. Subsequently, the CSE and HCT1 were cloned
into pK7WG2 final vector.
42
The vectors were inserted into Agrobacterium tumenfaciens strain EHA-105.
Colony PCR was used to confirm the presence of the vectors in the agrobacteria before
transformation (Figure 5). In addition, the vectors from HCT1 gene was used to produce the
multisite vector for the double mutant.
Figure 5. Colony PCR to confirm final vectors: pK7GW2-CSE1 (1-5); pK7GW2-CSE2 (6-
10) insertion in A.tumenfaciens. M – 1 Kb Ladder; B – Negative control; V1 – Vector pK7GW2-
CSE1; V2 – Vector pK7GW2-CSE2.
For the construction of the amiRNAs, we used a set of primers described in Table
8. The first step consisting of 3 PCRs: (a), (b) and (c) - Figure 6 A – were used as template as
the template for amiRNA production - Figure 7 B. After this step, we added the attB site by
PCR (Figure 6 C) using the primers AttB1 and AttB2 described in table 3. The PCR product
(Figure 6 C) was inserted into pDONR221 ™ entry vector and we developed colony PCR to
confirm the presence of amiRNAs in the vector (Figure 7). The insertion of the amiRNA into
the pK7GW2 vector was confirmed by PCR (Figure 8 - v1). Moreover, the vector was
transformed into A. tumenfaciens and colonies that were positive in colony PCR (Figure 8) were
used for the transformation of tobacco.
43
Table 4. Primers used for amiRNA design.
Primers Sequence
amiRNA
MIR319aA
Gtw
5’ GGGG ACA AGT TTG TAC AAA AAA GCA GGCTTC CTG
CAA GGC GAT TAA GTT GGG TAA C 3’
MIR319aB
Gtw
5’GGGG AC CAC TTT GTA CAA GAA AGC TGG GTT GCG GAT
AAC AAT TTC ACA CAG GAA ACA G 3’
CSE
CSETabmi
Rs-I
5’GATGTCATGTAAACAGTGCGCTTTCTCTCTTTTGTATTCC
3’
CSETabmi
Ra-II
5’GAAAGCGCACTGTTTACATGACATCAAAGAGAATCAATG
A 3’
CSETabmi
R*s-III
5’GAAAACGCACTGTTTTCATGACTTCACAGGTCGTGATATG
3’
CSETabmi
R*a-IV
5’GAAGTCATGAAAACAGTGCGTTTTCTACATATATATTCCT
3’
Figure 6. Construction of amiRNA by PCR. A) First-round - 3 reactions (a),(b),(c) M – 100
bp Ladder, 7-9 CSE (a)(b)(c) respectively; B) Second round PCR- used the first reaction as
template M – 1Kb Ladder; 3 – amiRNA CSE, 4 – Negative control; C) Addition of attB
overhang clone it by Gateway®, M – 1Kb, 1 – Negative Control, 4 – amiRNA CSE.
Figure 7. Colony PCR to confirm insertion of amiRNAs into pDONR221™ by Gateway®.
M – 100 bp Ladder; 1 – Negative Control; 12-16 –amiRNACSE.
44
Figure 8. Colony PCR to confirm amiRNACSE insertion the final into vector pK7GW2
in A. tumenfaciens. M – 1 Kb; B – Negative Control; V1 – Vector pK7GW2 amiRNACSE; 1-
5 Colonies from A.tumenfaciens with the construction pK7GW2 amiRNACSE.
We assembled a vector to contain two cassettes of expression, one to overexpress
HCT1 and the other to silence CSE: p35S::HCT-pCsVMV::amiRNACSE. For this, the vector
pK7GW2-HCT (p35S:: HCT) was digested with XbaI enzyme (Figure 9 A), and the vector
pXB2m43GW2 was digested with the same enzyme and the m43GW2 fragment was cut from
the gel (Figure 9 B). After assembled of both parts we developed colony PCR using the primers
(Table 5) to identify the one containing the insert in the desired orientation (Figure 10). Two
positive colonies were obtained for the pK7GW2-HCT construct fused to the m43GW2
fragment (Figure 11). The multisite vector was used in the subsequent steps for insertion of the
cassette pCsVMVamiRNACSE into the vector pK7GW2-HCT::m43GW2. The vectors were
cloned in E.coli DH10B by thermal shock and colony PCR was done with the primers
MIR319A and B (Table 4) to confirm if the construct was correct (Figures 9). After
confirmation, the vectors were inserted into A. tumenfaciens and the positive colonies were used
for tobacco transformation.
45
Figure 9. Digestion with the enzyme XbaI from the vector used to multisite assemble. A)
Vector pK7GW2-HCT (1); B) Vector pXB2m43GW2 (1-9). M- 1 Kb Plus.
Table 5. Primers used to check the correct orientation of the insert m43GW into
pK7GW2HCT:m43.
Primer Sequence
KanF 5' ACTCTAATTGGATACCGAGGGG 3'
m43R 5' GAGCTCGTTTTCCCAGTCAC 3'
Figure 10. Colony PCR to check the correct orientation of m43GW2 into pK7GW2HCT-
m43GW2, two E.coli colonies were tested. M- 1 Kb Plus; B – Negative Control; 1 – 4 Colony
1 with different temperatures Tm 55°C, 57°C, 60°C e 62°, respectively; 5-8 Colony 2 with
different temperatures Tm 55°C, 57°C, 60°C e 62°C respectively.
46
Figure 11. Colony PCR from final vector pK7GW2HCT-amiCSE to check if
pCsVMVamiRNACSE, was inserted. M- 1 Kb Plus; 1-10 pK7GW2HCT-amiCSE; 11 –
Positive Control (vector pCSVMVamiCSE).
3.3. Plant Transformation
The activity of CSE in lignin pathway has already been confirmed in several species
(Vanholme et al., 2013b; Ha et al., 2016; Saleme et al., 2017), and to confirm its importance in
tobacco we developed a series of mutants overexpressing and downregulating CSE. Moreover,
we developed a double mutant to overexpress HCT and downregulate CSE to try to recover the
dwarfism phenotype we found in plants downregulating CSE. The number of plants obtained
for each construction is described in Table 1.
Down-regulating CSE gene showed a severe impact in plant development
indicating this gene is essential for plant development in tobacco. These plants were severely
stunted. In culture media, we observed callus and leaves formation but stagnation of plant
development. To confirm the phenotype observed we performed five extra rounds of
transformation, all with around 200 explants each, and in all cases the phenotype found was the
same. These data indicate that product from CSE is essential for the normal development of
tobacco plants. The cse dwarf plants were cloned by tissue culture to obtain enough material to
perform qPCR in order to confirm the silencing of the CSE gene.
In contrast, plants overexpressing HCT and downregulating CSE (transformed with
the vector pK7GW2 HCT-amiCSE) showed normal development indicating that somehow the
HCT enzyme can reverse the cse phenotype and whatever is causing it. Indicating the possibility
of ensuring the production of caffeoyl CoA in the absence of CSE.
47
3.3. Gene expression in different tissues of WT and mutants of tobacco
3.3.1. Different tissues in Wild Type Plants
To better understand the balance between lignin and CGA biosynthesis we
quantified the relative expression of the key genes of the pathway: HCT, HQT, and CSE in
different tissue of WT 6-month-old tobacco plants (Figures 12 A-C). The level of expression
of CSE was 3.52 higher in young leaves and 3.29 higher in young stem than old stem (the lower
level of expression in this gene). Old leaves and roots have an intermediate pattern of expression
(Figure 12 A). HCT had the highest level of expression in the young stem and the old leaves,
the lowest. Roots and old stem also have a high level of expression than old leaves (Figure 12
B). In contrast, HQT had highest level of expression in young leaves, followed by old stem, and
the lowest in flower (Figure 12 C).
48
Figure 12. Gene expression in WT tobacco plants from different tissues. A) qPCR from
CSE gene; B) qPCR from HCT gene; C) qPCR from HQT gene. The letters represent the
different tissues used in the analyses: YS – Young stem; OS – old stem; F – flower; YL – Young
leaf; OL – old leaf; R – root. The bar represents the standard error of 4 replicates. The letters
represent the Tukey test and ANOVA statistical analyses with p<0.05 value. CNRQ =
Calibrated Normalized Relative Quantities.
A
B
C
CSE
HCT
HQT
49
3.3.2. qRT-PCR screening from double and single mutants
Plants downregulating CSE – both haplotypes at the same time – had growth stunted
and displaying a severe dwarf phenotype, similar to the phenotype reported for tobacco plants
downregulating HCT (Hoffmann et al., 2004). For this reason, these plants did not generate
decedents and we just analyzed T0 plants. The expression of HCT and HQT and CSE in these
plants are shown in Figure 13 and the relative level of expression of each gene can be seen in
table 6. Generally, these plants had low transcript level of CSE and HQT and no change in HCT
transcript levels.
Table 6. The transcript level of genes CSE, HCT, and HQT in cse mutants.
Tobacco
Lines CSE HCT HQT
WT 1.00 1.00 1.00
amiCSE90 0.54 1.54 0.37
amiCSE102 0.45 0.67 0.15
amiCSE78 0.37 1.25 0.34
amiCSE103 0.43 0.47 0.41
amiCSE31 0.41 0.95 0.37
amiCSE19 0.61 0.68 0.32
amiCSE91 0.86 2.83 0.50
amiCSE75 0.42 0.77 0.42
amiCSE101 0.40 0.92 0.34
50
Figure 13. Gene expression of CSE, HCT, and HQT by qRT-PCR of cse downregulated
tobacco plants. The bar represents the standard error of 3 replicates for WT plants, no statistical
analyses, and standard error was done to amiCSE lines since in T0 generation each line is
considered one independent line. CNRQ = Calibrated Normalized Relative Quantities.
We screened the other transgenic plants obtained – CSE and HCTamiCSE – (Table
1) by qRT-PCR to select the lineages to further analyses of T1 (Supplementary Figure 2). For
the double mutants (pK7WG2 HCTamiCSE) we screened 28 lineages and selected the lineages
1, 12 and 18. Plants overexpressing CSE1 and CSE2 were also screened, 9 of each haplotype.
Considering both haplotypes, only the transgenic overexpressing CSE2 showed high levels of
CSE expression, for this reason, we selected lineages 8, 9, 12 from CSE2.
In T1 generation we analyzed the relative expression of the transgenic lines
comparing it to wild type in two different tissues: stem and leaves. Although the transgenes
were transformed under the control of strong constitutive promoters, we decided to analyze
both tissues as lignin is mainly found in stem and CGA in leaves. The relative expression in
both tissues differed considerably for some transgenic lines and generally, the difference of
expression among WT and the transgenic plants were bigger in leaves than in the stem, (Figures
14 A- D). As expected, and opposite to CSE – except the event HCTamiCSE18 – the HCT
expression was increased in tobacco plants (Figure 14 A - B). Except for the expression in the
leaves of the event HCTamiCSE1, it was evident that CSE expression was decreased in tobacco
plants (Figures 15 C- D). The lowest and highest expression of CSE and HCT was observed in
the mutant HCTamiCSE12 (Figure 14 A-D).
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Re
lati
ve
Qu
an
titi
es
(CN
RQ
) CSE HCT HQT
51
Figure 14. Gene expression of double mutants in the T1 generation. A) HCT leaves; B)
HCT stem; C) CSE leaves; D) CSE stem. The letters and asterisk represent the Tukey test and
ANOVA statistical analyses with p<0.05 value, graphics without letters or asterisk means the
data have no statistical difference. The bar represents the standard error of 4 replicates. CNRQ
= Calibrated Normalized Relative Quantities.
ab
a bc
c
0
1
2
3
4
5
6
Re
lati
ve
Qu
an
titi
es
(CN
RQ
)
HCT Stem
*
00.20.40.60.8
11.21.41.6
Rela
tive
Qu
an
titi
es
(CN
RQ
)
CSE Gene expression qRT-PCR - Leaves
bcc
b
a
0.00.20.40.60.81.01.21.41.61.82.0
Re
lati
ve
Qu
an
titi
es
(CN
RQ
)
b
a
c c
0
2
4
6
8
10
12
14
16
18
Re
lati
ve
Qu
an
titi
es
(CN
RQ
)
A
B
C
D
52
As expected, the expression of CSE was increased in the tobacco plants transformed
with pCaMV35S::CSE (CSE overexpression). Even though the leaves of the event CSE8
showed a large variation (Figure 15 A), expression was increased in the stem and leaves of all
the events. CSE 8 was the line with the highest level of expression while CSE9 was the lowest
in both tissues (Figure 15 A – B).
Figure 15. Gene expression of overexpression mutants of CSE in the T1 generation. A)
CSE leaves; B) CSE stem. The letters represent the Tukey test and ANOVA statistical analyses
with p<0.05 value, graphics without letters means the data have no statistical difference. The
bar represents the standard error of 3 replicates for CSE9 and WT and 2 replicates for CSE12
and CSE8. CNRQ = Calibrated Normalized Relative Quantities.
ab
a
bc
c
0
5
10
15
20
25
30
35
40
45
Rela
tive
Qu
an
titi
es
(CN
RQ
)
a
b
ab
b
0
5
10
15
20
25
30
35
40
45
50
Rela
tive
Qu
an
titi
es
(CN
RQ
)
A
B
53
3.5. Phenolic Profiling
We studied also the phenolic profile in stems and leaves of CSE and HCTamiCSE
mutants (Figures 16 and 17, respectively). In the stem, except to quinic acid, all other phenolics
analyzed – shikimic acid, CGA and caffeic acid (CA) – had their content affected in the mutants
(Figure 16 A – D). CA content in stem increased in all lines, ranging from 33% to 51% more
than WT, with exception to HCTamiCSE18 that did not differ from WT (Figure 16 C).
Following the same pattern from CA, CGA content changed in all mutants, with exception to
HCTamiCSE18, with an increase of up to 53% more than WT (Figure 16 D). In the leaves
(Figure 17 A – D) there was a large variation for quinic and shikimic acids (Figure 17 A; B),
but a discrete increase was observed for caffeic acid (Figure 17 C) and a clear increase in CGAs
in both mutants (Figure 17 D). In general, the largest increases of CGA were observed in the
CSE mutants, where it was observed an increased up to 67% more than WT (Figure 17 D).
54
Figure 16. Phenolic profiling in the stem of transgenic lines. A) Quinic Acid; B) Shikimic
Acid; C) Caffeic Acid; D) Chlorogenic Acid. The letters represent the Tukey test and ANOVA
statistical analyses with p<0.05 value, graphics without letters means the data have no statistical
difference. The bar represents the standard error of 3 replicates for HCTamiCSE 1, 12 and 18;
CSE9 and WT and 2 replicates for CSE12 and CSE8.
ab
a a a a
b b
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
ug/m
g d
ry m
ass
0
5
10
15
20
25
ug/m
g d
ry m
ass
A
B
D
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
ug/m
g d
ry m
ass
C
0
2
4
6
8
10
12
14
16
ug/m
g d
ry m
ass
55
Figure 17. Phenolic profiling in leaves of transgenic lines. A) Quinic Acid; B) Shikimic
Acid; C) Cafeic Acid; D) Chlorogenic Acid. The letters represent the Tukey test and ANOVA
statistical analyses with p<0.05 value, graphics without letters means the data have no statistical
difference. The bar represents the standard error of 3 replicates for HCTamiCSE 1, 12 and 18;
CSE9 and WT and 2 replicates for CSE12 and CSE8.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
ug
/ m
g d
ry m
ass
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
ug
/mg
dry
ma
ss0
2
4
6
8
10
12
ug
/mg
dry
ma
ss
A
B
D
C
ab a
ab
bc bcac
c
0
5
10
15
20
25
30
ug
/mg
dry
ma
ss
56
3.6. Lignin content and composition
In order to understand how the production of lignin in tobacco stems was impacted
in CSE and HCTamiCSE mutants, we determined the total lignin content by acetyl bromide
method and quantified the lignin monomers – S, G, and H – to understand if an alteration in
lignin pathway could change lignin structure. Even though no statistically significant difference
was found, probably due variation among the replicates, we noticed a tendency among mutants
and double mutants. Compared to WT, lines HCTamiCSE12 and CSE12 had the most
pronounced change in total lignin content increasing 14% and 15% (Figure 18 A). These same
lines also had the S/G ratio more affected, 19%, and 16% higher than WT respectively (Figure
18 B). When we analyzed H monomers, we could observe decreasing in almost all lines, where
CSE 8 and 9 showing the lowest levels – 36% and 20% lower than WT. CSE 12, HCTamiCSE1
and 18 decreased 12%, 14%, and 11%, respectively, while HCTamiCSE 12 increased 9%
(Figure 18 C).
57
Figure 18. Lignin quantification in CSE mutants and HCTamiCSE double mutants
compared to WT. A) Total Lignin Content measured by acetyl bromide; B) S/G ratio; C)
Lignin Monomers. Tukey test and ANOVA statistical analyses with p<0.05 value but NO
statistical difference was found. The bar represents the standard error of 3 replicates for
HCTamiCSE 1, 12 and 18; CSE9 and WT and 2 replicates for CSE12 and CSE8.
0
10
20
30
40
50
60
nm
ol/100 m
g d
ry m
ass
S G H
00.10.20.30.40.50.60.70.80.9
1
S/G
Ra
tio
0
1
2
3
4
5
6
7
8
% C
ell W
all
A
B
C
58
3.7. Plant cell wall polysaccharides and saccharification
To estimate the impact of CSE and HCT activities change in the plant cell wall of
the mutants we analyzed the polysaccharides – cellulose, hemicellulose, and pectin. The highest
increase of cellulose was observed in CSE12, 35%. In the other mutants, the increase was
discrete and in line HCTamiCSE1 there was a decrease of 18% in cellulose (Figure 19 A).
Hemicellulose content in all CSE lines increased by 9% compared to WT while in double
mutants it ranged from 1% to 5% (Figure 19 B). Pectin level changed in CSE 9 and 12 –
decreased by 14 and 12% WT – and in HCTamiCSE1 – an increase of 6% (Figure 19 C).
Interestingly, all transgenic lines of HCTamiCSE had an increase in plant saccharification
efficiency. HCTamiCSE1 had 57%, HCTamiCSE18 had 24%, and HCTamiCSE12 had 19% of
increase compared to WT. On another hand, the saccharification efficiency was 15% (±1) lower
in all CSE lines (Figure 19 D).
59
Figure 19. Determination of plant cell wall polysaccharides and saccharification efficiency
of CSE mutants and HCTamiCSE double mutants. A) Cellulose; B) Hemicellulose; C)
Pectin; D) Percentage of saccharification. Tukey test and ANOVA statistical analyses with
p<0.05 value but no statistical difference were found. The bar represents the standard error of
3 replicates for HCTamiCSE 1, 12 and 18; CSE9 and WT and 2 replicates for CSE12 and CSE8.
0
5
10
15
20
25
30
mg/g
dry
mass
0
50
100
150
200
250
mg/g
dry
mass
abab
a
b
ab ab ab
0
20
40
60
80
100
120
140
160
180
mg/g
dry
mass
A
B
D
C
0
20
40
60
80
100
120
140
Eff
icie
ncy
%
60
3.8. Morphological and histochemical analyses
In order to verify if the mutants had changed their growth, we measured the
following parameters: plant height, internode length, leaf number, leaf area, fresh and dry
weight of T1 plants with one-year-old. The morphological characteristics observed did not
change statistically, probably due to a large variation we found in each group. For example, we
counted the number of leaves in all mutants and WT plants and while almost all lines of CSE
and HCTamiCSE ranged between 30 – 41 leaves the CSE8 line had 53 leaves, this means an
increase in 50% compared to WT – which had 35 leaves (Figure 20 A). Even though this
difference is considerable, when we look closely, we observed that both groups (CSE8 and WT)
had a large variation among the replicates – CSE8 ranged from 44 to 62 while WT ranged from
29 to 49. Leaf area reduced in all mutants, but it is more notable in CES8 line, 40% the average
found for WT leaf area (Figure 20 B). Both parameter, fresh mass and dry mass from leaves,
followed the same pattern (Figure 20 C – D). It did not significantly among the groups but
showed a tendency to decrease in CSE8 line.
The internode length (Figure 20 E) varied a lot in all lines ranging from 1 to 5 cm
while in double mutants it was more stable – an average of 3 cm – and tended to decrease
compared to WT – an average of 3.75 cm. The variation inside each replicate had high variation
but generally, it did not change compared with WT plants (Figure 20 F). The stem fresh and
dry mass did not change significantly among the groups analyzed and tended to decrease CSE9
line (Figure 20 G – H). Interestingly, CSE8 line, who showed a tendency to losing dry mass in
leaves did not the same pattern in stem.
61
Figure 20. Morphological analyses of CSE mutants and HCTamiCSE double mutants
compared to WT. A) Leaf Number; B) Leaf Area; C) Leaf Fresh Mass; D) Leaf Dry Mass; E)
Distance between internodes; F) Stem Height; G) Stem Fresh Mass; H) Stem Dry Mass. Tukey
test and ANOVA statistical analyses with p<0.05 value but NO statistical difference were
found. The bar represents the standard error of 3 replicates for HCTamiCSE 1, 12 and 18; CSE9
and WT and 2 replicates for CSE12 and CSE8.
A
B
C
0
10
20
30
40
50
60
70
Nu
mb
er
of
Leave
s
0
100
200
300
400
500
600
700
800
900
Leaf
Are
a (
cm2)
0
5
10
15
20
25
30
g F
resh
Mass
Leave
s
D
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
g D
ry M
ass
E
F
G
H
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
cm
0
10
20
30
40
50
60
70
cm
0
10
20
30
40
50
60
70
cm
0
10
20
30
40
50
60
70
cm
62
Plants downregulating CSE analyzed in T0 reached the maximum height after 1
month in culture and remained unchanged for the next 5 months (1 – 2 cm), as observed in
figure 21 A.
Figure 21. In vitro growth of cse mutants and WT after 2 months of transformation by A.
tumenfaciens. A) pK7GW2-amiRNACSE; B) Wild Type (WT).
Phloroglucinol-HCl stain showed a similar pattern of lignin in all plants analyzed.
Maüle staining showed a change of color from blood-red to brown-yellow in all transgenic lines
analyzed when comparing to WT indicating a decrease in S monomers (Figure 22 A – U) what
is not consistent with the analyses of lignin content and composition.
63
Figure 22. Histochemical analysis of lignin in transgenic tobacco lines. Cross-section of
stems from 7th internode from the top from the plant with 1year-old, WT, and transgenic lines.
A) WT colored with Phloroglucinol-HCl reagent 10X - 100nm; B) WT coloured with
Phloroglucinol-HCl reagent 20X - 20nm; C) WT coloured with Maüle reagent 20X – 20 nm;
D) CSE8 coloured with Phloroglucinol-HCl reagent 10X - 100nm; E) CSE8 coloured with
Phloroglucinol-HCl reagent 20X - 20nm; F) CSE8 coloured with Maüle reagent 20X – 20 nm;
G) HCTamiCSE1 coloured with Phloroglucinol-HCl reagent 10X - 100nm; H) HCTamiCSE1
coloured with Phloroglucinol-HCl reagent 20X - 20nm; I) HCTamiCSE1 coloured with Maüle
reagent 20X – 20 nm; J) CSE12 coloured with Phloroglucinol-HCl reagent 10X - 100nm; K)
CSE12 coloured with Phloroglucinol-HCl reagent 20X - 20nm; L) CSE12 coloured with Maüle
reagent 20X – 20 nm; M) HCTamiCSE12 coloured with Phloroglucinol-HCl reagent 10X -
100nm; N) HCTamiCSE12 coloured with Phloroglucinol-HCl reagent 20X - 20nm; O)
HCTamiCSE12 coloured with Maüle reagent – 20 nm; P) HCTamiCSE18 coloured with
Phloroglucinol-HCl reagent - 100nm; Q) HCTamiCSE18 coloured with Phloroglucinol-HCl
reagent 20X - 20nm; R) HCTamiCSE18 coloured with Maüle reagent – 20 nm; S) CSE9
coloured with Phloroglucinol-HCl reagent 10X - 100nm; T) CSE9 coloured with
Phloroglucinol-HCl reagent 20X - 20nm; U) CSE9 coloured with Maüle reagent – 20 nm
64
3.9. Pearson correlation and network analyses
We also performed Pearson correlation and network analysis from stem data of each
mutant group to access the level of association between the traits analyzed. The full data set of
correlation efficiency is shown as a heat map (Figure 23). In the stem of CSE mutants, a
statistically significant correlation was observed between cellulose and S/G (R=0.99), monomer
S (R=0.95) and CGA (R=0.96). Also, caffeic acid and shikimic acid displayed a positive
correlation (R=0.99), while pectin and S/G ratio (R=-0.97) showed a negative correlation.
When we analyze the HCTamiCSE double mutants, the metabolites connected differently, and
the statistically significant correlation was only found between quinic acid and hemicellulose
(R=0.97). However, while saccharification in CSE mutants was related with stem dry mass, the
network analyses for the HCTamiCSE mutants indicated that the changes in saccharification
efficiency were associated positively with pectin, hemicellulose, shikimic and quinic acid.
65
Figure 23. Correlation matrix based on Pearson coefficient derived from the average data
of stem for 1-year old CSE and HCTamiCSE mutants. Significant correlation coefficient
(p<0.05) are indicated by an asterisk, with positive and negative correlations being
distinguished by red and blue, respectively.
A Pearson correlation and network analyses were also established for leaves (Figure
24). For CSE mutants, no statistically significant correlation was found. Interestingly, in the
HCTamiCSE double mutants’ negative correlations among almost all the compounds analyzed
were observed: caffeic acid and quinic acid (R=-1.00); CGA and quinic acid (R=-0.98); CGA
and shikimic acid (R=0.96); CGA and dry mass (R=-0.97). Positive correlations were found
between shikimic acid and dry mass (R=0.98), and CGA and caffeic acid (0.97). This strong
correlation is highlighted in the network analyses (Figure 24).
CSE_Stem
ChlorogenicAcid
ShikimicAcid
Caffeic Acid
Cellulose
Pectins
S
G
QuinicAcid
H
Hemicellulose
Sacarification
S/G
Stem DW
Lignin
HCTamiCSE_Stem
Lignin
Cellulose
ChlorogenicAcid
Caffeic Acid
ShikimicAcid
QuinicAcid
S
G
H
Pectins
Sacarification
Hemicellulose
S/G
Stem DW
Cellulose 0.51
Chlorogenic Acid 0.48 0.96
Caffeic Acid 0.67 0.55 0.71
Shikimic Acid 0.61 0.62 0.78 0.99
Quinic Acid -0.47 0.45 0.54 0.14 0.26
S 0.75 0.95 0.91 0.66 0.69 0.18
G 0.79 0.93 0.91 0.72 0.75 0.15 1.00
H 0.31 -0.28 -0.49 -0.48 -0.56 -0.85 -0.11 -0.13
Pectins -0.65 -0.92 -0.80 -0.37 -0.41 -0.13 -0.94 -0.91 -0.11
Hemicellulose 0.34 0.61 0.80 0.91 0.95 0.54 0.59 0.63 -0.79 -0.31
Sacarification 0.12 0.46 0.21 -0.43 -0.39 -0.01 0.40 0.31 0.51 -0.68 -0.42
S/G 0.58 0.99 0.92 0.49 0.55 0.33 0.97 0.94 -0.13 -0.97 0.51 0.55
Stem DW -0.31 0.35 0.14 -0.59 -0.51 0.34 0.16 0.06 0.20 -0.45 -0.40 0.89 0.39
Lign
in %
Cel
lulo
se
Ch
loro
gen
ic A
cid
Caf
feic
Aci
d
Shik
imic
Aci
d
Qu
inic
Aci
d
S G H Pec
tin
s
Hem
icel
lulo
se
Saca
rifi
cati
on
S/G
Cellulose 0.56
Chlorogenic Acid 0.65 -0.22
Caffeic Acid 0.21 -0.68 0.86
Shikimic Acid 0.23 -0.54 0.54 0.72
Quinic Acid 0.20 -0.31 0.22 0.38 0.92
S 0.84 0.14 0.71 0.50 0.72 0.68
G 0.79 0.36 0.39 0.14 0.59 0.71 0.92
H 0.72 0.49 0.60 0.17 -0.30 -0.49 0.31 0.13
Pectins -0.71 -0.93 -0.11 0.41 0.51 0.43 -0.24 -0.33 -0.78
Hemicellulose -0.03 -0.41 0.05 0.30 0.86 0.97 0.49 0.56 -0.68 0.58
Sacarification -0.23 -0.79 0.23 0.61 0.89 0.83 0.33 0.23 -0.64 0.83 0.88
S/G 0.76 -0.11 0.94 0.78 0.71 0.50 0.91 0.67 0.46 -0.12 0.31 0.35
Stem DW -0.18 -0.46 0.45 0.54 -0.19 -0.56 -0.31 -0.65 0.43 0.15 -0.57 -0.12 0.12
Lign
in %
Cel
lulo
se
Ch
loro
gen
ic A
cid
Caf
feic
Aci
d
Shik
imic
Aci
d
Qu
inic
Aci
d
S G H Pec
tin
s
Hem
icel
lulo
se
Saca
rifi
cati
on
S/G
Positive 0.73-0.99 0.50-0.72 0.23-0.49 0.00-0.22
Negative 0.73-0.99 0.50-0.72 0.23-0.49 0.00-0.22
*
*
*
*
*
* *
*
66
Figure 24. Correlation matrix based on Pearson coefficient derived from the average data
of leaves for 1-year old CSE and HCTamiCSE mutants. Significant correlation coefficient
(p<0.05) are indicated by an asterisk, with positive and negative correlations being
distinguished by red and blue, respectively.
3.10. Principal Components Analysis
In order to identify notable differences between mutants and WT plants we analyzed
all data by Principal Components Analysis - PCA (Figure 25). The first principal component
(PC1) and the second principal component (PC2) accounted for 39.9% and 20.2% of the total
variation, respectively. By PCA score plots we could identify three groups. The first group is
the WT contrasting with profiling of CSE mutant and HCTamiCSE double mutants – CSE
mutants are the second group and HCTamiCSE the third group. Interestingly, the line 9 behaved
differently than the other two lines of CSE mutants, as it grouped together with the double
mutants. One of the main contributors for the formation of WT and CSE8/CES12 groups were
Positive 0.73-0.99 0.50-0.72 0.23-0.49 0.00-0.22
Negative 0.73-0.99 0.50-0.72 0.23-0.49 0.00-0.22
Caffeic Acid 0.85
Quinic Acid -0.58 -0.06
Shikimic Acid 0.13 0.55 0.68
Leaf DW -0.88 -0.85 0.28 -0.51
Ch
loro
gen
ic A
cid
Caf
feic
Aci
d
Qu
inic
Aci
d
Shik
imic
Aci
d
CSE_Leaf
ChlorogenicAcid
Caffeic Acid
QuinicAcid
Leaf DW
ShikimicAcid
Caffeic Acid 0.97
Quinic Acid -0.98 -1.00
Shikimic Acid -0.96 -0.91 0.94
Leaf DW -0.97 -0.89 0.91 0.98
Ch
loro
gen
ic A
cid
Caf
feic
Aci
d
Qu
inic
Aci
d
Shik
imic
Aci
d
HCTamiCSE_Leaf
ChlorogenicAcid
Caffeic Acid
QuinicAcidShikimic
Acid
Leaf DW
67
the higher levels of S and G monomers, and chlorogenic acid (both organs), caffeic acid (in
leaves) and hemicellulose in the this two CSE mutant lines. On the other hand, saccharification
level, quinic acid, and shikimic acid, both from stem provided a massive contribution for the
separation of the HCTamiCSE double mutants from all other transgenic plants we analyzed.
Figure 25. Principal component analyses (PCA) for transgenic plants and WT. PCA was
performed on the correlation matrix of least square means. The number in parentheses give the
percentage variation explained by the first (PC1 – 39.9%) and the second components (PC2 –
20.2%) which together comprise 60.1% of the total variance. A) shows the score plot where the
circle colors indicate the clusters to which metabolite was assigned using hierarchical cluster
formed by Pearson distance (red WT; green double mutants and CSE 9; yellow CSE 8 and
CSE12); B) shows the loading plots obtained for the resulting of the distribution of the analysed
parameters.
68
4. Discussion
4.1. Search of HCT and CSE alleles and expression profile
The enzymes CSE and HCT are positioned at the beginning of the lignin pathway
and they were described as related with the production of G and S subunits (Vanholme et al.,
2013; Hoffmann et al. 2003). Hoffmann et al. (2004) first proved HCT activity in planta by
producing gene-silenced A. thaliana and Nicotiana benthamiana. CSE had its activity proved
more recently in A. thaliana by (Vanholme et al., 2013b). Here we search HCT and CSE
sequences in the databases available and found 4 haplotypes for HCT and 2 for CSE.
Considering that N. tabacum is an allotetraploid (2n=4x=48) that evolved from interspecific
hybridization between the ancestors Nicotiana sylvestris (2n=24) and Nicotiana
tomentosiformis (2n=24) (Leitch et al., 2008), it was expected to find up to four different
haplotypes for each locus. It was also analyzed the protein domains for all genes. For all HCT
we found the same motifs, characteristic of BAHD superfamily, group from which HCT
belongs. Similarly, for CSE it was also found the same motifs for both haplotypes, all of them
are in accordance with the characteristics of this enzyme as previously described (Gao et al.,
2010; Vijayaraj et al., 2012).
It is well known that CGA and lignin metabolic pathways share common
intermediates but the connection between CGA and lignin pathway are still unclear. It has been
suggested that CGA route probably acts as carbon donor to lignin pathway (Días et al., 1997;
Comino et al., 2009; Joët et al., 2009; Escamilla-Treviño et al., 2014). Another important
question is related to a balance between the pathways, i.e., is there a competition by
intermediates in these pathways? To answer this question first it is necessary to investigate if
the key genes involved in both metabolic pathways are expressed in the same organ or if they
have different expression pattern. For this reason, we analyzed the expression of the three genes
(HQT, HCT, and CSE) involved in the biosynthesis of CGA and lignin in different organs of
WT plants. According to Niggeweg et al., (2004), 98% of the CGA produced in tobacco is
produced by HQT. They also found evidence indicating that most of CGA in tobacco is found
in leaves. We found relatively high levels of HQT transcripts in leaves followed by old stem
(Figure 12 C). High levels of HQT was also found in potato leaves and skin tuber by Payyavula
et al., (2015). As an antioxidant, the CGA levels in the old part of the stem may be related to
the accumulation of reactive oxygen species in old tissues (Petrov et al., 2015). HCT transcripts
were mostly found in the young stem (Figure 12 B), probably because of the high activity of
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lignin metabolism during the formation of the vascular tissue (F. Evert, 2006). The expression
of HCT was also expressive in roots where xylem formation is important for water transport.
High level of lignin in roots and its importance to root development has been previously
reported (Abiven et al., 2011; Naseer et al., 2012; Zhao et al., 2013). It is noteworthy that HCT
and HQT showed an opposite pattern of expression, indicating a balance between lignin and
CGA routes. Interestingly, while HCT was more expressed in the stem (mostly in the young
stem) and HQT more expressed in young leaves, CSE has an intermediate pattern of expression,
i.e., it is found mostly expressed in young leaves and young stem respectively (Figure 12 A). A
positive correlation between CSE and HQT transcripts has been previously described in potato
tubers (Valiñas et al., 2015). Differently of we found here, in potato tuber CSE seems to have
a stronger relationship with HQT than with HCT, suggesting greater importance of CSE in CGA
instead of lignin pathway in this species (Valiñas et al., 2015). In tobacco this relationship
seems to be different, as our results suggest that CSE pattern of expression have similarities
with both genes (HCT and HQT), suggesting an involvement in both pathways.
4.2. Downregulation of CSE severely impact plant development
To better understand the impact of CSE in tobacco, we developed tobacco cse
mutants. The most significant characteristic of cse mutants was the dwarf phenotype with
growth and development severely affected (Figure 21 A). Defect in plant growth and
development caused by manipulation in lignin metabolism has been reported for several genes:
CCR1 (Ruel et al., 2009); HCT (Hoffmann et al., 2004; Shadle et al., 2007); C3’H (Franke et
al., 2002b; Takeda et al., 2018); C4H (Schilmiller et al., 2009) including for Medicago
truncatula cse mutants (Ha et al., 2016). Ruel et al., (2009) developed ccr1 Arabidopsis mutants
with a severe dwarfed phenotype and using transmission electron microscopy observed a strong
collapse of xylem cells. Recently, Meester et al., (2018) developed ccr1 ProSNBE: CCR1 able
to overcome this dwarfed phenotype described earlier using a vessel-specific promoter and
developed viable plants.
It has been argued that lignin dwarfed phenotype may have a collapse of conducting
vessels, making the development of these plants impossible (Pereira et al., 2018). Lignin
provides to the vascular system mechanical strength, stiffness and hydrophobicity to support
gravity, mechanical stress and negative pressure generated by perspiration allowing the
transport of water and solutes along with the plant (Ferrer et al., 2008; Pereira et al., 2018).
Recently, the dwarf phenotype was described as lignin modification-induced dwarfism - LMID
(for a recent review see Muro-Villanueva, Mao and Chapple, 2019) and was related with 1)
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collapse of conducting vessels; 2) accumulation of an intermediate or derivates compounds of
phenylpropanoid pathway; 3) changes in the integrity of plant cell wall structure. Taking into
account the second hypothesis, downregulation of CSE may affect the flow of the
phenylpropanoid pathway and other secondary metabolites may have their biosynthesis blocked
or overstimulated.
Our cse mutants have the caffeic acid production locked and this compound has
been described as the major regulator of monolignols biosynthesis (Wang et al., 2014a). Caffeic
acid content is responsible for regulating the expression of key enzymes of the phenylpropanoid
pathway such as PAL and 4CL (Wang et al., 2014a). The inhibition of PAL activity could lead
to an over-accumulation of cinnamic acid, which in excess could lead to dwarfism (Vanholme
et al., 2019a). The reduction in C4H activity leads to a dwarfed phenotype (Schilmiller et al.,
2009) and their drastic reduction in lignin metabolism was associated to an increase in cinnamic
acid derivates (Van de Wouwer et al., 2016). Perturbations in auxin signaling have been
associated with LMID and trans-cinnamic acid can act as an anti-auxin compound (Schilmiller
et al., 2009; Bonawitz and Chapple, 2013).
The CSE role in the lignin pathway was already investigated in A. thaliana, M.
truncatula, dicot, Leguminosae), poplar (Populus deltoides, dicot, Salicaceae), and switchgrass
(Panicum virgatum, monocot, Poaceae) (Vanholme et al., 2013b; Ha et al., 2016; Vargas et al.,
2016; Saleme et al., 2017). Although in all these species CSE has a clear role in the lignin
pathway, in each species CSE seems to have a different level of importance. Poplar cse mutants
did not show any phenotype abnormality compared to WT, whereas cse mutant of M. truncatula
plants was severely dwarfed (Ha et al., 2016; Saleme et al., 2017), similar to the phenotype we
obtained in tobacco. These data indicate that CSE is the preferable route for the biosynthesis of
caffeoyl CoA in the lignin pathway for these species. Ha et al., (2016) produced CSE loss of
function mutants using transposon insertion and observed in M. truncatula severe dwarfing,
altered development, reduction in lignin content, and preferential accumulation of
hydroxyphenyl (H) units. Even though in Arabidopsis cse mutants did not present severe
dwarfism, the plants were 37% smaller than WT and had an increase in up to 30% accumulation
of H units (Vanholme et al., 2013b). The same authors recovered normal phenotype in
Arabidopsis cse mutants using a vessel-specific promoter to drive CSE expression, suggesting
that at least in this species, the phenotype found was probably caused by the collapse of
conducting vessels. The differences in the impact of CSE downregulation in different species
might be a consequence of the HCT efficiency to produce caffeoyl CoA to the lignin
biosynthesis (Ha et al., 2016). Here, we could recover WT phenotype overexpressing HCT
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using a constitute promoter (pCaMV35S). Furthermore, besides dwarf phenotypes we also
observed a reduction of H units mainly in plants over-expressing CSE (CSE lines) and less in
the HCTamiCSE lines (Figure 18 C), indicating that HCT might be partially restoring H
biosynthesis.
To gain information about our mutant, we analyzed the level of expression of CSE,
HCT, and HQT by qRT-PCR in the cse mutants and although we did not find significant
changes in HCT transcript level, the expression of HQT was on average 64% reduced compared
to WT expression (Figure 13). As HQT has been suggested as the main enzyme in the
biosynthesis of CGA in tobacco (Niggeweg et al., 2004), this data suggests that CGA
metabolism was directly affected and it might be involved in dwarfing induction in the cse
mutants. Most of the known CGAs are conjugates of quinic acid and caffeic acid (Clifford,
1999), and an unbalance in caffeic acid could affect HQT level of expression by negative
feedback. Furthermore, these results are in accordance with 1) our analysis of qRT-PCR in
different plant organs (Figure 12), when we could clearly see a pattern of expression that
connects this gene to both CGA and lignin metabolic pathways, and 2) with the mutants
overexpressing CSE, that showed accumulation in CGA content (Figure 17 D).
4.3. HCT overexpression overcome cse dwarfism and CSE mutants accumulate
CGA without affecting lignin content
Interestingly, when we associate downregulation of CSE with overexpression of
HCT in the HCTamiCSE mutants, we obtained plants close to the normal phenotype of WT
plants. Despite a loss in up to 39% of dry mass (Figures 20 D and 20 H), our double mutants
generated fertile plants, which flowered at the same time of WT plants. This is a proof that at
least in tobacco, HCT is able to convert caffeoyl shikimate into caffeoyl CoA in planta, a
reaction that has only been shown before in vitro and indicates a clear preference in the reverse
reaction indicating this reaction could not happen in vivo (Hoffmann et al., 2004; Vanholme et
al., 2013b; Escamilla-Treviño et al., 2014; Wang et al., 2014a).
The phenolic profiling from these mutants showed that even though both mutants –
HCTamiCSE and CSE lines – showed their caffeic acid content affected in stem, the CGA was
only affected in leaves of the CSE mutants. Caffeic acid content increased in the stem of all
CSE mutants’ lines but it was only statistically significant for the lines CSE 9 and 12 (Figure
16 C). This compound also accumulated in the double mutants (lines HCTamiCSE1 and 12),
but less than the CSE mutants, even though these plants presented very low levels of expression
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of CSE. Whether and at which rate HCT and CSE operate modulating the formation of caffeoyl-
CoA is unknown in plants harbouring both enzyme activities. Tobacco hct mutants showed an
increase in CGA content in the stem indicating its involvement in CGA catabolism instead of
biosynthesis (Hoffmann et al., 2004). The caffeic acid accumulation in our double-mutants may
be related with a lower efficiency of HCT to convert caffeoyl CoA to CGA acid, thus inhibiting
the conversion of caffeic acid by 4CL. Escamilla-Treviño et al., (2014) showed in switchgrass
that two recombinant HCTs (PvHCT1a and PvHCT2a) displayed in vitro activity to convert
efficiently caffeoyl-shikimate to caffeoyl CoA, while an HCT-like (PvHCT-Like1) was able to
convert caffeoyl CoA to CGA, thus exhibiting HQT activity and preferring quinic acid as acyl
acceptor. The recombinant PvHCTLike1 was less efficient to catalyse the formation of CGA
from caffeoyl CoA than the reaction using 4-coumaroyl CoA and quinic acid to form 4-
coumaroyl quinate. Thus, we speculate that even the double mutants had over-expression of
HCT and inhibition of CSE, the low affinity of HCT for caffeoyl CoA may have led to the
inhibition of 4CL since it was observed an accumulation of caffeoyl CoA. This may also explain
the reason CSE and HCTamiCSE mutants accumulate caffeic acid. The accumulation of caffeic
acid also argues against a possible activity of CSE using CGA as substrate (see Figure 2 in
introduction).
Our double mutants downregulating cse and overexpressing HCT (HCTamiCSE)
did not have their CGA content significantly affected (Figure 17 D). However, the mutants
overexpressing CSE had their CGA content affected with an increase in up to 67% in leaves of
CSE9 line (Figure 17 D). This accumulation can be the result of a redirection of the excess of
caffeic acid produced by CSE overexpression, and CGA may serve as a carbon reservoir for the
excedant carbon flowing in the lignin metabolism. The increase in the stem was less expressive,
on average 31% compared with the WT plants (Figure 16 D). The major accumulation of caffeic
acid in stem instead of leaves is an indication that CGA was more actively synthesized in the
leaves, although quinic acid had not changed its level. In fact, caffeic acid is used both for CGA
and lignin biosynthesis. Additionally, the greater accumulation of CGA in leaves instead stem
could be explained by the fact that more HQT transcripts were found in leaves than in stem
(Figure 12 C). HQT is the preferable enzyme for CGA synthesis in tobacco (Niggeweg et al.,
2004) and for this reason, an excess of substrate could favor CGA production in leaves instead
of stem. It is noteworthy, that caffeic acid concentration might be a key point in lignin metabolic
flux. The excess of caffeic acid has been associated with inhibition of PAL and 4CL (Wang et
al., 2014a; Van de Wouwer et al., 2016). Once it is blocked, the metabolic pathway cannot flow
73
to the lignin pathway until the excess was re-routed. This data may explain why we did not find
difference in lignin content for both mutants and indicates that the flow of carbon in the lignin
pathway is finely regulated.
Our correlation and network analyses in stem of CSE mutants found a positive
correlation between caffeic acid and shikimic acid content (R= 0.99 – Figure 23 A). Shikimate
also increased in the stem of the mutants indicating that whatever the same protein or not of the
reaction caffeoyl CoA → CGA, the accumulation of this phenolic is a strong indication that
CSE was the main route for lignin biosynthesis in tobacco, thus efficiently draining caffeoyl
CoA for monomers synthesis. The excess of caffeic acid produced in stem leads to an
overproduction of shikimic, which explains the over-accumulation of this intermediate
(Vanholme et al., 2019b). Shikimate esters intermediates are not essential for the biosynthesis
of monolignols but have been considered as the preferred substrate for C3H though, this way it
is possible that shikimic acid act in monolignol regulation (Vanholme et al., 2019b).
In spite of the changes in levels of caffeic and shikimic acids, there was not a change
in quinic acid. The Pearson correlation and network analyses from leaves (Figure 23) identified
a negative correlation between caffeic acid and quinic acid content (R=-1) in the double mutants
and this correlation have no significance in CSE mutants. This data indicates that although both
mutants have the same branch engineered, quinic and shikimate branches were affected
differently in leaves.
It is noteworthy, that transgenic lines with the highest level of CSE transcripts level
(lines CSE8 and 12 were 23 and 36-fold higher than the WT – Figure 15) are more contrasting
between themselves than when they are compared to WT. They also tended to accumulate more
caffeic acid and CGA (Figure 25). Differently of these lines, CSE9 tended to accumulate more
lignin then CGA and had less CSE expression (9-fold than the WT – Figure 15). Thus, it seems
that an overflow of the CSE branch favors CGA biosynthesis probably because of an increase
in the amounts of the intermediates. The balance, however, between the two routes for the
biosynthesis of caffeoyl CoA seems to be more complex as the double mutant with the highest
level of expression of HCT (19-fold than WT) and the lowest level of expression of CSE (0,1-
fold than WT – Figure 14) – HCTamiCSE12 – tended to be closer to the CSE lines and
accumulate more CGA while the other two double mutants with the lower expression level of
HCT, tend to accumulate more lignin.
Differently from what expected, our mutants did not increase massively lignin
content or had the ratio S/G changed (Figure 18 A – B). The lignin content showed large
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variation among replicates from both mutants – CSE and HCTamiCSE – and it did not
significantly differ from WT. The same happened to the S/G ratio. Alteration in lignin content
associated with an increase in H monomer due to C3H, HCT or CSE manipulation has been
described in the literature in several species but these works usually analyses downregulation
of these genes (Hoffmann et al., 2004; Shadle et al., 2007; Vanholme et al., 2013a; Vanholme
et al., 2013b; Tong et al., 2015; Ha et al., 2016; Ponniah et al., 2017; Saleme et al., 2017; Zhou
et al., 2018). Thus, we expected a decrease in H units in our mutants, especially in the CSE
mutants. Indeed, H units’ relative amount slightly decreased in CSE mutants, with an average
of 22%, although it was not statistically significant (Figure 18 C). Arabidopsis cse-2 loss-of-
function mutants had a decrease of 36% in total lignin content associated with an increase of
30X in H units in lignin polymer (Vanholme et al., 2013b). M. truncalata cse loss-of-function
mutants had a reduction in 80% of lignin with an increase of 50X of H unit relative amount (Ha
et al., 2016). Downregulation of HCT in alfalfa led to a reduction of lignin in up to 50%
associated with an increase in H unit into lignin polymer (Shadle et al., 2007). Saleme et al.,
(2017) described a reduction of 25% in lignin deposition associated with an increase of 113%
of H units in lignin polymer in Poplar cse mutants. Considering that there are differences in the
level of downregulation of these genes and differences among species, it is well accepted that
downregulation of genes positioned at the beginning of S and G metabolic branch can be
compensated by an overproduction of H subunit. Counterintuitively, the overexpression of F5H
driven by C4H promoter in poplar did not change total lignin content, it changed instead the
S/G ratio and lignin polymer was constituted mainly by S units (97.5%) (Stewart et al., 2009).
Highlighting the complexity of lignin pathway, our results showed that, lignin biosynthesis is
finely regulated in tobacco and the excess of carbon generated by overstimulation of shikimate
shunt in the mutants were reallocated into CGA. The correlation and network analysis in stem
of CSE mutants (Figure 23 A) showed a positive correlation between the monomer S and CGA
content (R=0.96), supporting the hypothesis that the accumulation of CGA did not affect the
production of lignin biosynthesis although a possible over stimulation of the pathway was in
part flowed into CGA.
Even though the saccharification efficiency did not differ between mutants and WT,
the CSE and HCTamiCSE plants had the opposite pattern of saccharification efficiency (Figure
19 D). Generally, CSE lines had lower saccharification efficiency than WT, whereas double
mutants improved saccharification efficiency in up to 57% in HCTamiCSE1 (Figure 19 D).
Furthermore, their cellulose content seems to follow the inverse pattern, while CSE lines
increased cellulose content, double mutants decreased it. This is especially pronounced
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comparing CSE12 and HCTamiCSE1, one increased their cellulose content by 35% whereas
the other decreased it by 19% (Figure 19 A). Generally, downregulation of C3H, HCT and CSE
increased saccharification efficiency and the cellulose content increased (Tong et al., 2015; Ha
et al., 2016; Vargas et al., 2016; Saleme et al., 2017; Zhou et al., 2018). Arabidopsis cse mutants
though, had saccharification efficiency increased and cellulose content decreased (Vanholme
et al., 2013b), the same response found in our mutants, i.e. an inverse relation between
saccharification and cellulose. Even though an increase in cellulose content is usually
associated with an increase in saccharification, changes in its crystalline structure can reduce
biomass degradability (Marriott et al., 2015; Van de Wouwer et al., 2016). Cellulose is a linear
polymer formed by β-1-4 linked glucopyranosyl residues, and adjacent residues are rotated 180°
to maintain linearity leading to a highly rigid and insoluble crystalline region (Van de Wouwer
et al., 2016). This crystallinity difficult the access of hydrolytic enzymes increasing biomass
recalcitrance (Marriott et al., 2015). Thus, increasing cellulose crystallinity could reduce cell
wall saccharification. Marriott et al., (2014) analyzed several mutants with enhanced
saccharification efficiency from Brachypodium distachyon and showed that it is not necessarily
associated with changes in lignin content. Sac1 and sac7 mutants did not change lignin content
but instead had a decrease in crystalline cellulose content associated with their increase in
saccharification. In addition, sac7 increased its total polysaccharide content whereas sac1 had
its cell-wall ester-bound ferulic acid decreased – which could affect the cross-linking between
cellulose with hemicellulose and lignin (Marriott et al., 2014). The correlation and network
analyses from stem of CSE mutants showed a positive correlation between S/G ratio and
cellulose content (R = 0.99) suggesting that the alterations in cellulose content in our mutants
may be caused by the perturbation in lignin metabolism.
In summary, our data indicate that there is no competition for the common
intermediates between CGA and lignin biosynthesis, while the first seems to be produced
mainly in leaves by HQT, the other occurs mainly in the stem by HCT. These enzymes seem to
be a key point to determine the flux, while both enzymes showed a very characteristic pattern
of expression, CSE showed a pattern of expression that suggests its involvement in both
pathways. Supporting this hypothesis, our CSE mutants had the excess of carbon generated by
overexpression of CSE remobilized into CGA in leaves but not in stem. It is tempting to
hypothesize that these differences in carbon remobilization observed between leaves and stem
are part of a strategy to finely regulate lignin branch since the excess of caffeic acid can inhibit
lignin route. Additionally, the overexpression of HCT in plants downregulating CSE recovered
the dwarfed phenotype completely indicating that HCT is capable to produce caffeoyl CoA
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from caffeoyl shikimate. This fact is remarkable since it is the first time that the ability of HCT
to convert caffeoyl shikimate was proved in planta. The differences found between the mutants
might be due to the pool of caffeoyl CoA available. CSE is more efficient in the conversion of
caffeoyl shikimate into caffeoyl CoA and possible the main route of production. For this reason,
it accumulates more caffeoyl CoA increasing its viability to CGA pathway. In another hand,
HCT conversion of caffeoyl shikimate to caffeoyl CoA is not so efficient and this way, lower
levels of caffeoyl CoA would be available to remobilized into CGA. Even though these genes
are part of the same branch in phenolic pathway they may have different roles in plant
metabolism and trigger different responses upon perturbation. Lignin metabolic flux seems to
be finely regulated in tobacco, since overexpression of shikimic branch does not affect
drastically lignin content. The accumulation of caffeic acid in both mutants is an indicator that
this compound might be a key point to regulate lignin pathway in tobacco. To conclude, our
data indicates that CSE is the preferable route for biosynthesis of caffeoyl CoA in tobacco and
its overproduction favor CGA pathway in leaves. On the other hand, HCT is critical for lignin
metabolism and its overexpression associated with cse downregulation does not seem to affect
CGA pool (i.e., probably due its low affinity to quinate), this enzyme is also capable to produce
Caffeoyl CoA even though it does not seem to be the preferable route of biosynthesis.
Acknowledgments
NVS thanks São Paulo Research Foundation for a doctoral fellowship (Processo FAPESP
n°2014/17831-5). This study was financed in part by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.
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Chapter 3
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CRISPR/Cas9 genome editing to modify lignin biosynthesis in Nicotiana tabacum
Nathalia Volpi e Silva1,2, Ewerton Ribeiro1, Felipe Thadeu Tolentino1 Oleg Raitskin2, Nicola J
Patron2, Paulo Mazzafera1.
1 – Universidade Estadual de Campinas; 2 – Earlham Institute
Abstract
Genome editing using clustered regulatory interspaced short palindromic repeats/CRISPR-
associated protein 9 system (CRISPR/Cas9) has been proved as a powerful tool in genome
editing and promises to revolutionize the use of biotechnology for crop breeding. CRISPR/Cas9
allows the development of biallelic homozygous in T0 and open a new horizon in crop breeding
for the possibility to develop "transgenic-free" genome-edited mutants. Here, we propose the
genome editing of tobacco (Nicotiana tabacum) using CRISPR/Cas9 technology in order to
understand the carbon flow in lignin metabolism through chlorogenic acid (CGA). We are using
a key gene in lignin and chlorogenic acid (CGA) pathways, Caffeoyl CoA O-methyltransferase
(CCoAOMT), hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (HCT)
and caffeoyl shikimate esterase (CSE), as a target to induce mutagenesis. Besides cellulose,
lignin represents the main compound in plant cell wall composition and a major challenge in
biomass processing, such as the production of second-generation bioethanol. On the other hand,
CGA is part of a group of phenolic antioxidants highly important for human dietary and plant
defense response. These two biosynthesis routes are inter-connected by caffeoyl CoA and
probably CGA acts as a carbon skeleton donor to lignin metabolism. We developed double and
single mutants for hydroxycinnamoyl-CoA shikimate hydroxycinnamoyl transferase (HCT), and
caffeoyl shikimate esterase (CSE) using traditional transformation methods to prove this
hypothesis. We successfully assembled and validated the constructions with two sgRNAs and
Cas9 by Agro-transient assay in tobacco leaves. Transiently we were successful in inducing
specific mutations in tobacco in both haplotypes of CSE and in two HCT haplotypes. This
approach enables the development of stable plants in order to overcome the dwarfism in HCT
silenced plants inducing mutation in only two of the four haplotypes present in tobacco. Due to
CRISPR/Cas9 specificity, it is possible to inactivate only specific haplotypes and isoforms
individually very precisely. This approach would help us to understand the role of HCT in lignin
and CGA pathways. In conclusion, our vectors enable the development of CCoAOMT, CSE and
HCT genome-edited stable plants to give us more information to confirm if CGA and lignin are
inter-connected.
1. Introduction
CRISPR system was discovered in prokaryotes as an antiviral defense mechanism,
found in ~40% of bacteria and ~90% of archaea, acting as an adaptive immune system against
phages or conjugative plasmids through horizontal gene transfer (Waters and Storz, 2009; Kaya
et al., 2016). In prokaryotes, CRISPR loci are a cluster of short repeats sequence separated by
a short spacer sequence with the invader DNA added during infections (Figure 1a). During a
new infection, a new spacer relative to the foreign DNA is integrated into the endogenous
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CRISPR array, conferring resistance to subsequent infections (Waters and Storz, 2009;
Marraffini, 2015). Adjacent to CRISPR loci is an operon of CRISPR associated (CAS) genes
that encode Cas proteins (Figure 1a). These endonucleases are guided by the space sequences
in the CRISPR-RNA to cleave the invader genome - Figure 1a (Waters and Storz, 2009; Belhaj
et al., 2015; Marraffini, 2015). Several CRISPR/Cas immunity systems have been described,
but type II has been the most extensively studied (Marraffini, 2015). Recently, the type II
nuclease system originated from Streptococcus pyogenes was engineered to be used as a tool
for genome editing in eukaryotes - Figure 1b (Pan et al., 2016).
The CRISPR/Cas type II system is comprised of a Cas9 endonuclease protein, two
small RNAs - the CRISPR-RNA (crRNA) and the trans-activating crRNA (tracrRNA) - and a
protospacer adjacent motif (PAM sequence of 5’-NGG-3’) downstream of the target sequence,
Figure 1a (Li et al., 2013; Mahfouz et al., 2014; Belhaj et al., 2015; Marraffini, 2015; Pan et al.,
2016). The CRISPR/Cas9 protein is guided by a duplex formed by the two small RNAs to
cleave the complementary sequence (Figure 1a). Recently, the duplex has been fused to form a
single chimeric guide RNA molecule (gRNA) containing a 20-nucleotide (nt) sequence to
mediate targeting information - Figure 1b (Mahfouz et al., 2014; Belhaj et al., 2015; Pan et al.,
2016). This finding makes CRISPR/Cas9 technology a promising tool for genome editing
considering that theoretically any genomic sequence bearing a PAM could be simply and easily
engineered (Li et al., 2013; Mahfouz et al., 2014; Ding et al., 2016). As PAM sequences are
highly frequent in genomes, virtually any gene could be targeted (Ding et al., 2016). Due to the
simple design of target specificity and its compact nature, CRISPR/Cas9 allows simultaneous
targeting of multiple gene loci (Lowder et al., 2015; Ma et al., 2015). Lowder et al., (2015)
described the simultaneous editing of up to eight different targets. The group developed a
toolbox that allows the transcriptional activation or repression of plant endogenous genes for
monocots and dicots. Similarly, Ma et al., (2015) edited 46 target sites in rice with a high
percentage of mutation, which was mostly biallelic and homozygous. They also provided loss-
of-function mutations by simultaneous targeting all members of a given gene family, either
genes from the same pathway or different targets in the same gene for both rice and Arabidopsis.
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Figure 1. CRISPR from prokaryotes immunity system and as a biotechnological tool for
genome editing. (a) CRISPR/Cas9 type II system in prokaryotes. The pre-crRNA is transcribed
from CRISPR region and it is processed by Cas9, RNaseIII and the tracrRNA. The tracrRNA
base-pairs with repeat region of pre-crRNA so the RNaseIII can cleave it. Cas9 is guided by the
crRNA to recognize the protospacer sequence in the foreign DNA and induces a double-break.
(b) The use of CRISPR/cas9 as a tool, in which the crRNA and the tracrRNA are fused into a
single guide RNA (sgRNA). In this remodeled system, the target is a specific region in the
genome of the target species. To enable the movement of Cas9 into the eukaryotic nuclear
compartment, a nuclear localization signal is added. This figure is from Belhaj et al., (2015).
Editing genome in plants using CRISPR/Cas9 has been successfully applied in
Arabidopsis thaliana (Jiang et al., 2013; Li et al., 2013; Fauser et al., 2014; Lowder et al., 2015),
Nicotiana tabacum (Gao et al., 2015), Nicotiana benthamiana (Jiang et al., 2013; Nekrasov et
al., 2013; Lowder et al., 2015), rice (Jiang et al., 2013; Lowder et al., 2015), wheat (Wang et
al., 2014b), maize (Feng et al., 2016), sorghum (Jiang et al., 2013), petunia (Zhang et al., 2016),
barley (Lawrenson et al., 2015), tomato (Brooks, C., Nekrasov, V., Lippman, Z.B. and Van
Eck, 2014; Pan et al., 2016), Populus (Zhou et al., 2015), soybean (Cai et al., 2015; Jacobs et
al., 2015), Brassica oleracea (Lawrenson et al., 2015) and sweet orange (Jia and Nian, 2014).
Tobacco plants (N. tabacum) edited via CRISPR/Cas9 were obtained with a mutation rate of
81.8%, a high level of biallelic mutations and no significant off-targets (Gao et al., 2015).
An advantage of using CRISPR/Cas9 in basic and applied research is the possibility
to produce homozygous and biallelic mutations already in the first generation (Xu et al., 2015;
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Zhou et al., 2015; Osakabe et al., 2016a), drastically reducing the time for functional analyses
comparing to conventional transgenic approaches. Because the CRISPR/Cas9-induced
mutations are transmitted to the next generation (Chen et al., 2001; Feng et al., 2014; Schiml et
al., 2014; Belhaj et al., 2015; Ding et al., 2016; Osakabe et al., 2016b) this genome-editing tool
can be used to accelerate breeding of elite clones. Osakabe et al., (2016b) was able to increase
the heritable efficiency in Arabidopsis thaliana using a truncated gRNA (tru-gRNA) guided-
Cas9 driven by a specific-promoter with high expression in germline in order to improve the
heritable pattern. The use of tru-gRNA was a strategy to avoid off-targets, a usual issue in
genome editing technologies.
Another advantage of this technology is the development of “transgenic-free” plants
carrying heritable biallelic mutations (Woo et al., 2015; Xu et al., 2015; Zhou et al., 2015). Woo
et al., (2015) reported the generation of genome-edited Arabidopsis, tobacco and lettuce plants
without introducing foreign DNA into plant cells and had up to 46% of mutants in regenerated
plants. Even though the study shows an unpredictable pattern of biallelic mutations in T0 lines,
the mutations in T1 lines were stably transmitted to the next generations. In addition, the
transgene from transgenic plants containing CRISPR/Cas9 can be segregated out and
independently from the edited region, generating “transgene-clean” and homozygous plants for
the desired mutation (Xu et al., 2015). Zhou et al., (2015) obtained a poplar plant with a CRISPR
biallelic mutation for an important gene from the lignin biosynthesis pathway – 4-
coumarate:CoA ligase (4CL). It was the first lignin-related gene engineered via CRISPR/Cas9.
Having the advantage of no direct influence from the T-DNA insertion, the group was able to
analyze the effect of the edited mutation in 30 independent lines. The results showed a reduction
in lignin content and in S/G ratio in 4cl1 mutants with high levels of efficiency and phenotypic
reproducibility, usually not found with the conventional techniques previously used,
highlighting the robustness of genome editing by CRISPR/Cas9 (Zhou et al., 2015).
Here, we genome-edited tobacco-cells by agro-transient method to lignin-related
genes: CSE, CCoAOMT, and HCT. The construction and validation of these constructs open
the possibility to develop stable plants with CRISPR/Cas9 to induce mutations to lignin genes
in tobacco. Moreover, we believe this way we could overcome the dwarfism previously
described in tobacco silenced to HCT (Hoffmann et al., 2004). The N.tabacum genome is
allotetraploid (2n=4x=48) (Leitch et al., 2008), therefore, it is expected to contain four
different haplotypes for each locus. CRISPR/Cas9 shows high specificity, allowing the accurate
targeting of only one haplotype. This strategy is particularly interesting if the idea is to
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understand the role of different haplotypes individually. Genome editing by CRISPR/Cas9
allows the development of more reproducible and accurate results, besides the generation of
“transgene-free” plants, which would imply in higher acceptance by the general public (Belhaj
et al., 2015; Lowder et al., 2015; Ma et al., 2015; Tong et al., 2015; Zhou et al., 2015). The use
of this technique can be applied to improve the production of second-generation ethanol and
food with high levels of antioxidants such as CGAs in a safer way and with better public
acceptance than with the use of conventional transgenic strategies.
2. Material and Methods
2.1. Target locus selection and sgRNA design
The sgRNA design was done using the gene sequence from Nicotiana tabacum
obtained from NCBI (National Center for Biotechnology Information) and SOL Genomics
(Bombarely et al., 2011) databases. After the identification of the gene sequences, the design
of the sgRNA followed the criteria described by (Parry et al., 2016). One important point to
consider when designing sgRNA is to have 100% identity between the seed sequence and the
sgRNA. Another important point is to identify the PAM sequence, the pattern necessary for
CRISPR/Cas9 to create the cut and enable the mutation. This region is constituted of the NGG
sequence and was used to identify possible targets (Xie et al., 2014). Subsequently, the search
for potential off-targets was done via Benchling (Benchling Inc., 2018 - https://benchling.com/)
and CRISPR RGEN Tools (Bae et al., 2014). The absence of off-targets is crucial considering
we want to target a specific locus. Moreover, the presence of non-CpG sensitive restriction site
sequence is also important to evaluate because they are predicted to be disrupted by Cas9
induced indels (Lawrenson et al., 2015).
2.2. Construct DNA assembly and multiplex targeting
Binary plasmid vector constructs were assembled using Golden Gate Modular
Cloning (MoClo) as described by (Engler et al., 2014; Lawrenson et al., 2015). NtHCT,
NtCCoAOMT, and NtCSE were targeted through the development of both single and multiple
haplotypes editing. Each sgRNA was designed following the criteria described by (Parry et al.,
2016).
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2.3. Agroinfiltration: Development, test, and delivery of the constructions in
tobacco leaves
To test the targeted mutagenesis of the tobacco genome, transient expression by
agroinfiltration was employed. Two-months-old plants cultivated in pots with a capacity of 5
kg soil in a greenhouse were used to carry the experiment. Leaf tissue from tobacco was agro-
infiltrated following the protocol described by (Nekrasov et al., 2013) with modifications. After
bacterial incubation, the O.D600 was adjusted to 0.1. The leaf was infiltrated with the solution
and incubated in the dark for 2 days in B.O.D in a temperature of 21ºC, after this period the
plants were transferred to a B.O.D in a photoperiod of 12 light / dark hours and a temperature
of 25 ° C. The leaves were collected 6 days after agro-infection and the genomic DNA was
extracted using Qiagen’s DNeasy Plant DNA Extraction Mini Kit.
2.4. Genotyping
The genomic DNA of the leaf tissue was analyzed by PCR/RE method as described
by (Nekrasov et al., 2013) with modifications. Using the restriction enzyme site loss method,
the genomic DNA was digested and PCR reaction with primers flanking the target site to detect
mutations was performed. The presence of edited sequences was increased in the PCR product
which then could be sequenced to identify mutations.
3. Results and Discussion
3.1. Target locus selection and sgRNA design
3.1.1. HCT
To design the sgRNA, we used as references the bioinformatic analysis from
Chapter 2. The analysis was done in the SOL genomics database using the sequence described
by Hoffmann et al., (2003) as reference. The results revealed the presence of four haplotypes
of HCT in N.tabacum genome as shown in the Supplementary Table 2. The next step was to
identify from which ancestor each haplotype has originated from. To achieve this goal, BlastN
in SOL Genomics database between the haplotypes was performed and it was created a matrix
of identity with the haplotypes and the HCTs found in the ancestors’ genomes using BioEdit,
shown in Supplementary Table 5. The matrix of identity showed the identity between the
sequences analyzed, we used this matrix with the purpose of identifying from which ancestor
each allele probably came from and this way try to group the alleles in groups in order to design
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the specific sgRNA. It was expected to find two haplotypes closer from N. tomentosiformis and
two haplotypes closer from N. sylvestri, instead of that we found HCT 1, 2 and 4 closers from
N. tomentosiformis and HCT 2 closer from N. sylvestri. While the haplotypes HCT 1 and 2
present high identity with gene 21152, the HCT 4 have high identity with gene 29186, both
from N. tomentosiformis. For this reason, we used the identity between the alleles from
N.tabacum as criteria to form two groups (Figure 2): Group 1 (HCTg1), formed by the
haplotype HCT 3 and 4 with the identity of 0.0473 and Group 2 (HCTg2), formed by HCT 1
and 2 with the identity of 0.0449. In addition to that and in order to select the best group to
target we also performed qPCR for both groups to see if they were differentially expressed in
leaves and stem, no significant variation was found (Supplementary Figure 3).
Figure 2. Alignment of HCT isoforms to identify the similarity among the haplotypes. The
program Benchling Inc., 2018 was used to align the sequences and the algorithm used was
MAFFT. The sequences showed inside the blue square represent the group1 while the
sequences inside the red square represent group 2.
After defining the groups of haplotypes, their sequences were aligned and the
pattern of CRISPR/Cas9 target sites NGG, the PAM, was searched. Although Benchling Inc.,
2018 (https://benchling.com/) offers a tool to design sgRNA for N.tabacum, we decided to
design the sgRNAs manually because we wanted to target two haplotypes per plant, while the
program considers the corresponding haplotype as an off-target. As mentioned before, the
region upstream of the PAM (20 nt) is the seed sequence and it needs to be 100% identical to
the sgRNA recognition sequence. For this reason, we selected regions that were identical
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between the haplotypes within a given group. In addition, to avoid off-targets, we used CRISPR
RGEN Tools Cas-OFFinder (Bae et al., 2014) that searches for off-targets in the N. tabacum
genome.
Since the best target was selected the next step was to select the sequence to the
sgRNA forward primer scaffold. The scaffold is variable depending on which template is used
for the next step and on the promoter used. In this case, the Arabidopsis U6-26 promoter (the
template pICSL9002) was used. The forward primers are in table 1:
Table 1. Primers and position in the gene to create the sgRNAs for all haplotypes of HCT.
Target 20nt Selected
Gene
region
bp
sgRNA F Primer sgRNA
Name
HCT3
and
HCT4
ACCCCAAGTGTT
TACTTTTAC 127-147
tgtggtctca ATTG
ACCCCAAGTGTTTACTTTTAC
gttttagagctagaaatagcaag
sgRNA.
1.1
HCT3
and
HCT4
AACACTTGGGGT
GTGGAAATT 116-138
tgtggtctca ATTG
AACACTTGGGGTGTGGAAATT
gttttagagctagaaatagcaag
sgRNA.
1.2
HCT3
and
HCT4
AAGACGCCGGA
GTTCCAAAGT 355-375
tgtggtctca ATTG
AAGACGCCGGAGTTCCAAAGT
gttttagagctagaaatagcaag
sgRNA.
1.4
HCT3
and
HCT4
TTGGTGATTTTG
CGCCTACTT 338-358
tgtggtctca ATTG
TTGGTGATTTTGCGCCTACTT
gttttagagctagaaatagcaag
sgRNA.
1.6
HCT1
and
HCT2
ACTTTTCCGTCG
AAGAAATTT 144-164
tgtggtctca ATTG
ACTTTTCCGTCGAAGAAATTT
gttttagagctagaaatagcaag
sgRNA
2.2
HCT1
and
HCT2
CACTTGTGCCGT
TTTATCCTA 188-208
tgtggtctca ATTG
CACTTGTGCCGTTTTATCCTA
gttttagagctagaaatagcaag
sgRNA
2.3
HCT1
and
HCT2
CAACGGCGGGG
ATGAGTTGA 345-364
tgtggtctca ATTG
CAACGGCGGGGATGAGTTGA
gttttagagctagaaatagcaag
sgRNA
2.6
HCT1
and
HCT2
CCCGCCGTTGAT
TACTCACA 355-374
tgtggtctca ATTG
CCCGCCGTTGATTACTCACA
gttttagagctagaaatagcaag
sgRNA
2.7
The reverse primer is the same (tgtggtctca AGCGTAATGCCAACTTTGTAC ) for
all sgRNAs. The vector pICSL70001 contains the sgRNA scaffold. Amplification with the
primer pair from the previous step resulted in a PCR product that is the specific sgRNA as
shown in Figure 3.
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Figure 3. PCR amplification with the vector pICSL70001 containing the sgRNA scaffold
and the sgRNA primer designed for each sgRNA from HCT and CSE; Tm= 60°C. M -
Marker NEB 2 -log; 1 - sgRNA 1.1; 2 – sgRNA 1.2; 3 – sgRNA 1.4; 4 – sgRNA 1.6; 5 –
sgRNA 2.3; 6 – sgRNA 2.6; 7 – sgRNA 2.7; 10 – sgRNA 3.3; 11 – sgRNA 3.4; 12 – sgRNA
4.1; 13 – sgRNA 4.2; 14 – sgRNA 4.4; 15 – sgRNA 4.5.
The PCR amplification that did not work were repeated with a different annealing
temperature (from 60°C to 58°C) and shown in Figure 4.
Figure 4. PCR amplification with the vector pICSL70001 containing the sgRNA scaffold
and the sgRNA primer designed for each sgRNA from HCT and CSE; Tm= 58°C. M -
Marker NEB 2 -log; 1 – sgRNA 1.2; 2 – sgRNA 1.4; 3 – sgRNA 2.2.; 4 – sgRNA 2.6; 7 –
sgRNA4.1; 8 – sgRNA 4.4.
3.1.2. CCoAOMT
CCoAOMT have been classified into three different classes (Maury et al., 2002).
We decided to focus on class I because it is the first expressed during stem development (Maury
et al., 2002). The presence of four isoforms of CCoAOMT from class I have been described
already by Martz et al., (1998). Therefore, this previous was used work as a reference for our
analyzes. In fact, the presence of extra isoforms or haplotypes in the tobacco genome were
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checked by BlastN searching in the SOL Genomics database, but no other sequences were
found. The alignment of CCoAOMT isoforms pointed a low identity level among them, making
it difficult to design sgRNA with 100% identity with all isoforms. Consequently, we decided
to design sgRNA to target each isoform individually. Considering the different results found
when the expression of different CCoAOMT isoforms was manipulated (Zhong et al., 1998;
Pinçon et al., 2001), it would be interesting to determine the potential role of one specific
isoform in both pathways: lignin and CGA.
Accordingly, to design the sgRNA specific for each isoform a tool offered by
Benchling Inc., 2018 (https://benchling.com/) was used, this tool analyzes the best targeting
region for each gene taking into consideration the match between the sequence and the sgRNA.
It also analyzes the possible off-targets through a screening of the N.tabacum genome. The
targets selected can be seen in Table 2. We used the same strategy previously described to create
the sgRNA for CCoAOMT isoforms and Table 2 shows the forward primers.
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Table 2. Primers and position in the gene to create the sgRNAs for all haplotypes of CCoAOMT.
Target 20nt Selected Gene
region sgRNA F Primer
sgRNA
Name
CCoAOMT 1 and 2
TGCCATCATCGGGAAGAGCCA
272-292
tgtggtctca ATTG TGCCATCATCGGGAAGAGCCA
gttttagagctagaaatagcaag
sgRNA5.1
CCoAOMT 3
TCATCAATGCCAAAAACACAA
206-226
tgtggtctca ATTG TCATCAATGCCAAAAACACAA gttttagagctagaaatagcaag
sgRNA5.5
CCoAOMT 1 and 2
TCATCGGCAGAGGTGGTCATG
156-176
tgtggtctca ATTG TCATCGGCAGAGGTGGTCATG
gttttagagctagaaatagcaag
sgRNA5.6
CCoAOMT 3
TCATCAGCAGAGGTGGTCATG
156-176
tgtggtctca ATTG TCATCAGCAGAGGTGGTCATG
gttttagagctagaaatagcaag
sgRNA5.7
CCoAOMT 1
CTTAGCTCTTTCATGGGCTC
-106 tgtggtctca ATTG CTTAGCTCTTTCATGGGCTC
gttttagagctagaaatagcaag sgRNA11
.1
CCoAOMT 1
AGATCACCGCAAAACACCCC
147 tgtggtctca ATTG AGATCACCGCAAAACACCCC
gttttagagctagaaatagcaag sgRNA11
.2
CCoAOMT 2
TTCTTTCGTTGGTGTTGAAG
-32 tgtggtctca ATTG TTCTTTCGTTGGTGTTGAAG
gttttagagctagaaatagcaag sgRNA10
.1
CCoAOMT 2
AATGGAAGACATCAAGAAGT
98 tgtggtctca ATTG AATGGAAGACATCAAGAAGT
gttttagagctagaaatagcaag sgRNA10
.2
CCoAOMT 3
GAGGTGGTCATGATGTTCCA
-157 tgtggtctca ATT GAGGTGGTCATGATGTTCCA
gttttagagctagaaatagcaag sgRNA9.
1
CCoAOMT 3
TGACCACCTCTGCTGATGAA
186 tgtggtctca ATTG TGACCACCTCTGCTGATGAA
gttttagagctagaaatagcaag sgRNA9.
2
CCoAOMT 3
CATCAATGCCAAAAACACAA
235 tgtggtctca ATTG CATCAATGCCAAAAACACAA
gttttagagctagaaatagcaag sgRNA9.
3
CCoAOMT 4
CACTGGTTTCAAGTATGTAC
-72 tgtggtctca ATTG CACTGGTTTCAAGTATGTAC
gttttagagctagaaatagcaag sgRNA8.
1
CCoAOMT 4
ATCCATGAAAGAGCTCAGGG
130 tgtggtctca ATTG ATCCATGAAAGAGCTCAGGG
gttttagagctagaaatagcaag sgRNA8.
4
CCoAOMT 4
AGGTGACTGCTAAGCATCCA
150 tgtggtctca ATTG AGGTGACTGCTAAGCATCCA
gttttagagctagaaatagcaag sgRNA8.
5
The vector pICSL70001 contains the sgRNA scaffold. Amplification with the
primers pair from the previous step resulted in a PCR product that is the specific sgRNA as
shown in Figure 5.
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Figure 5. PCR amplification with the vector pICSL70001 containing the sgRNA scaffold
and the sgRNA primer designed for each sgRNA from CCoAOMT 1 -4; Tm= 60°C. M -
Marker NEB 2 -log; 1 – sgRNA 5.1; 2 – sgRNA 5.5; 3 – sgRNA 5.6.; 4 – sgRNA 5.7; 5 –
sgRNA 8.1; 6 – sgRNA 8.4; 7 – sgRNA 8.5; 8 – sgRNA 9.1; 9 – sgRNA 9.2; 10 – sgRNA 9.3;
11 – sgRNA 10.1; 12 – sgRNA 10.2; 13 – sgRNA 11.1 14 – sgRNA 11.2.
3.1.3. CSE
The caffeoyl shikimate esterase (CSE) is an enzyme recently discovered as part of
the lignin pathway (Vanholme et al., 2013b). We have been already characterizing the role of
CSE in lignin biosynthesis in tobacco in Chapter 2. Our previous results showed that strong
CSE down-regulation leads to a similar phenotype found in HCT downregulated plants
(Hoffmann et al., 2004) (Results showed in Chapter 2). In order to confirm the dwarfed
phenotype found in Chapter 2 is due the downregulation o CSE and is not related to the use of
amiRNA, one of the advantages of using genome editing by CRISPR/Cas9 is that the results
are not affected by the position where the transgene was inserted since it can be easily removed
by Mendelian segregation. For this reason, we decided to create CRISPR/Cas9 mutants to edit
both CSE and validate these results.
The sgRNA design strategy was the same as previously described for HCT although
the goal was to target both CSE haplotypes (described in detail in the Chapter 2) at once. Table
3 describes the forward primers from sgRNA designed:
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Table 3. Primers and position in the gene to create the sgRNAs for all haplotypes of CSE.
Target 20nt Selected Gene
region sgRNA F Primer
sgRNA
Name
CSE ATCTTACTTCGAAACACCCAA 93-113
tgtggtctca ATTG ATCTTACTTCGAAACACCCAA gttttagagctagaaatagcaag sgRNA4.1
CSE ATTGAGTGAAGAGCTTGCCGT 113-133
tgtggtctca ATTG ATTGAGTGAAGAGCTTGCCGT gttttagagctagaaatagcaag sgRNA4.2
CSE GGCTACGGTTCCGATACCGGT 190-210
tgtggtctca ATT GGCTACGGTTCCGATACCGGT gttttagagctagaaatagcaag sgRNA4.4
CSE CCGGTATCGGAACCGTAGCCA 189-209
tgtggtctca ATTG CCGGTATCGGAACCGTAGCCA gttttagagctagaaatagcaag sgRNA4.5
The vector pICSL70001 contains the sgRNA scaffold. Amplification with the
primers pair from the previous step resulted in a PCR product that is the specific sgRNA as
shown in Figure 4.
3.2. Construct DNA assembly and multiplex targeting
To assemble CRISPR/Cas9 with the sgRNA we used the Golden Gate Modular
Cloning Toolkit (MoClo) (Addgene kit # 1000000044; Addgene kit # 1000000047). This tool
increases the speed of assembling of multiple genetic elements (Engler et al., 2008; Weber et
al., 2011; Werner et al., 2012; Engler et al., 2014; Marillonnet and Werner, 2015). This method
is based on the use of type IIS restriction enzymes, BsaI and BpI, combined with restriction-
ligation to create multiple gene constructs with just a few reactions and is based on different
vector levels: Level 0, Level 1, Level 2 (Engler et al., 2008).
Level 0 is the entry-level used to clone the promoter, terminator, and gene
individually. The next level is Level 1, a binary vector where it is necessary to insert the
promoter with the gene of interest and the terminator together by the combination of Level 0
constructs. There are seven positions of Level 1 vectors and its position will determine the
position of each construct in Level 2 vectors, depending how many constructs do you need to
assemble (Engler et al., 2008; Weber et al., 2011; Werner et al., 2012; Engler et al., 2014;
Marillonnet and Werner, 2015). Consequently, it is important to design the final construct
before start cloning into Level 1 vectors. To assemble our sgRNA with the hCas9 protein (Mali
et al., 2013) the first step was to design the final construct to determine in which position of the
Level 1 each gene or sgRNA should be cloned (Table 4). Then we had to determine which
resistance gene would be chosen for the final construct, a construct Level1 containing the nptII
gene fused with Nos promoter was used. Furthermore, a construct Level 1 with the Cas9
described by Mali et al., (2013) fused with the CaMV35S promoter (pICSL11021). The level 1
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containing sgRNA was done fusing the sgRNA with the U6-26 promoter from Arabidopsis
thaliana (Lawrenson et al., 2015). Level 0 containing the promoter and terminator were selected
from the existing library and assembled with the sgRNA to create the Level 1 U6-26::sgRNA.
The PCR to confirm the insertion of U6-26::sgRNA into Level 1 can be seen in Figure 5 and 6.
Table 4. Experiment design to assemble Level 1 vectors.
Position Gene Name of vector sgRNA name
1 Kan, CCoAOMT3 pICH47732 5.5; 9.3
2 Cas9 pICH47742 -
3 HCT1, CSE, CCoAOMT1,2,3 pICH47751 1.1; 1.4; 4.1; 5.1; 5.7; 9.1; 9.2; 11.1
4 HCT1, CSE, CCoAOMT1,2,3 pICH47761 1.2; 1.; 4.2; 5.5; 5.6; 9.3; 11.2
5 HCT2, CCoAOMT2,4 pICH47772 2.2; 2.6; 4.4; 8.1; 10.1
6 HCT2, CCoAOMT4 pICH47781 2.3; 2.7; 4.5;8.4; 8.5; 10.2
7 CCoAOMT3 pICH47791 5.7;9.1;9.2
Figure 5. Colony PCR amplification from Level 1 vectors contains the U6-26 fused with
HCT and CSE. Tm= 60°C. M – NEB 2-log; 1 – sgRNA 1.1; 2 – sgRNA 1.2 colony 1; 3 –
sgRNA 1.2 colony 2; 4 – sgRNA 1.4 colony 1; 5 – sgRNA 1.4 colony 2; 6 – sgRNA 1.6; 7 –
sgRNA 2.2 colony 1; 8 – sgRNA 2.2 colony 2; 9 – sgRNA 2.3 colony 1; 10 – sgRNA 2.3 colony
2; 11 – sgRNA 2.6 colony1; 12 – sgRNA 2.6 colony 2; 13 – sgRNA 2.7 colony 1; 14 – sgRNA
2.7 colony 2; 22 – sgRNA 4.1 colony 1; 23 – sgRNA 4.1 colony 2; 24 – sgRNA 4.2 colony 1;
25 – sgRNA 4.4 colony 1; 26 – sgRNA 4.4 colony 2; 27 – sgRNA 4.5 colony 1; 28 – sgRNA
4.5 colony 2.
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Figure 6. Colony PCR amplification from Level 1 vectors contains the U6-26 fused with
CCoAOMT 1-4. Tm= 60°C. M – NEB 2-log; 1 – sgRNA 5.1; 2 – sgRNA 5.1 colony 1; 3 –
sgRNA 5.5 colony 2; 4 – sgRNA 5.6; 5 – sgRNA 5.7 colony 1; 6 – sgRNA 5.7 colony 2; 7 –
sgRNA 8.1; 8 – sgRNA 8.4; 9 – sgRNA 8.5; 10 – sgRNA 9.1 colony 1; 11 – sgRNA 9.1 colony
2; 12 – sgRNA 9.2 colony 1; 13 – sgRNA 9.2 colony 2; 14 – sgRNA 9.3 colony 1; 15 – sgRNA
9.3 colony 2; 16 – sgRNA 10.1; 17 – sgRNA 10.2; 18 – sgRNA 11.1; 19 – sgRNA 11.2; 22 –
Blank.
The next level would be Level 2. For the constructs containing only 4 sgRNA, we
used Level 2 as the final binary level (Figure 7). In Level 2 it is possible to join 6 different
modules of Level 1 to create one Level 2 (Engler et al., 2014; Marillonnet and Werner, 2015).
To add more than 6 Level 1 modules, it is necessary to use Level M. The Level M works exactly
like the level 2, but it is not the final vector, there are 7 Level M vectors that can be combined
to form one Level P, the final binary vector (Weber et al., 2011; Werner et al., 2012). We used
a combination of two sgRNA per haplotype distant enough (100 bp) to create deletions to
produce truncated proteins. The sgRNAs were designed at the beginning of the mRNA to
guarantee the protein produced after the deletion would be non-functional. The design of Level
2 is shown in Figure 8 and the combination of sgRNAs used in each construction can be seen
in Supplementary Table 6.
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Figure 7. Schematic of binary vectors delivered in N.tabacum by agro-transient assay and
target sequence. A) The tobacco construct has a neomycin phosphotransferase II (NptII) gene
driven and terminated by Nopaline Synthase promoter (pNOS) and terminator (T-NOS); a Cas9
expression cassette consists in the sequence of Cas9 from S. pyogenes human codon-optimized
(SpCas9h) driven and terminated by 35S promoter and terminator; and two single guide RNA
(sgRNA) driven by A. thaliana U6 promoter; B) The gene model are represented by two exons
(blue boxes) and one intron (blue line), the sgRNA are shown below target region in exon one
in the same region of recognition of restriction enzymes (RE).
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Figure 8. Schematic of all binary vectors delivered in N.tabacum by agro-transient assay.
All constructs have a neomycin phosphotransferase II (NptII) gene driven and terminated by
Nopaline Synthase promoter (pNOS) and terminator (T-NOS); a Cas9 expression cassette
consisting in the sequence of Cas9 from S. pyogenes human codon-optimized (SpCas9h) driven
and terminated by 35S promoter and terminator; and two single guide RNA (sgRNA) driven by
A. thaliana U6 promoter. The figure (A – E) represent all constructions tested to target HCT
gene; (F) represent the construction tested to target CSE genes and (G – N) represent all the
constructions tested to target CCoAOMT.
Level 2 was assembled, and the positive colonies were mini-prepped and digested
with the enzyme HindIII for 1 hour at 37°C (Figure 9, 10 and 11). The plasmid with the correct
pattern of digestion was sequenced and introduced into Agrobacterium tumenfaciens for further
tests.
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Figure 9. Digestion of Level 2 constructs for HCT and CSE with the enzyme HindIII to
confirm the assembly of Level 1. M – NEB 2-log; A1 – Level 2 A colony 1; A2 – Level 2 A
colony 2; B1 – Level 2 B colony 1; C1 – Level 2 C colony 1; C2 – Level 2 C colony 2; D1 –
Level 2 D colony 1; D2 – Level 2 D colony 2; E1 – Level 2 E colony 1; E2 – Level 2 E colony
2; F1 – Level 2 F colony 1; F2 – Level 2 F colony 2.
The plasmids from construct E did not work and four new colonies were selected
and digested (Figure 10).
Figure 10. Digestion of Level 2 constructs for HCT and CSE with the enzyme HindIII to
confirm the assembly of Level 1. M – NEB 2-log; E3 – Level 2 E colony 3; E4 – Level 2 E
colony 4; E5 – Level 2 E colony 5; E6 – Level 2 E colony 6.
Figure 11. Digestion of Level 2 constructs for CCoAOMT 1-4 with the enzyme HindIII to
confirm the assembly of Level 1. M – NEB 2-log; M1 – Level 2 M colony 1; I1 – Level 2 I
colony 1; I2 – Level 2 I colony 2; I3 – Level 2 I colony 3; J1 – Level 2 J colony 1; J2 – Level
2 J colony 2; K1 – Level 2 K colony 1; M2 – Level 2 M colony 2.
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Figures 9, 10 and 11 show the Level 2 constructs for HCT and CSE which were
properly produced. According to Martz et al., (1998), CCoAOMT3 and CCoAOMT4 are the
isoforms most expressed in vascular tissue, suggesting that these isoforms are likely the most
important for lignification. CCoAOMT2 was more expressed in flowers (Martz et al., 1998).
Accordingly, the subsequent experiments were carried out with the constructs successfully
produced.
3.3. Agroinfiltration and Genotyping
To evaluate the efficiency of the designed sgRNAs we performed Agroinfiltration
in tobacco leaves. The plants were cultivated in greenhouse conditions for 4 weeks before the
Agro-infiltration, and 4 replicates were used per group. Before assembling the Level 2 vectors,
we performed the infiltration with different mixtures of Level 1 vectors (Supplementary Table
7) to test which would be the most efficient combination to construct the Level 2 vectors and
to evaluate whether the sgRNAs were working properly. Three days after infiltration, we
extracted the genomic DNA to perform PCR amplification and detect deletions.
Figure 12. PCR using as template the genomic DNA of leaves after the transient
expression assay. The name of each group can be found in table 11; each sample corresponds
to a mix of the four replicates. M – NEB 2-log; 1 – blank targeted with primers from HCT4; 2
– WT DNA targeted with primers from HCT4; 3 – TE 1 targeted with primers from HCT4; 4 –
TE 2 targeted with primers from HCT4; 5 – Blank targeted with primers from HCT3; 6 – WT
DNA targeted with primers HCT3; 7 – TE 1 targeted with primers HCT3; 8 – TE 2 targeted
with primers HCT3; 9 – Blank targeted with primers HCT1; 10 – WT DNA targeted with
primers HCT1; 11 – TE 3 targeted with primers HCT1; 12 – TE 4 targeted with primers HCT1;
13 - Blank targeted with primers HCT2; 14 – WT DNA targeted with primers HCT2; 15 – TE
3 targeted with primers HCT2; 16 – TE 4 targeted with primers HCT2; 17 – Blank targeted with
primers CSE1; 18 – WT DNA targeted with primers CSE1; 19 – T5 targeted with primers CSE1;
20 – T6 targeted with primers CSE1; 21 – T7 targeted with primers CSE1; 22 – Blank targeted
with primers CSE2; 23 – WT DNA targeted with primers CSE2; 24 – T5 targeted with primers
CSE2; 25 – T6 targeted with primers CSE2; 26 – T7 targeted with primers CSE2.
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We could not detect any mutation by PCR (Figure 12). Therefore, we decided to
use the restriction enzyme site loss method (RE) to enrich the DNA with the edited sequence.
In this strategy, we digested the PCR product with a restriction enzyme that cuts in the region
to be edited (Supplementary Table 7). Previous studies showed that Cas9 cuts around 3 or 4 bp
upstream of the PAM (Lawrenson et al., 2015). If we had success in the editing, the sequence
would lose the recognition site for the RE and it would not be cut (Nekrasov et al., 2013;
Lawrenson et al., 2015). After the digestion with the RE, the PCR was submitted to PCR again.
Even after the digestion, we could not identify any mutation (Supplementary Figure 4).
After analyzing the results, it was impossible to conclude whether the sgRNA was
working or not and for this reason we decided to change our strategy: 1) we had mixed Level 1
vectors to perform the Agro-infiltration, which could affect the efficiency of the transient assay,
this way, to guarantee that the Cas9 protein and the sgRNA were delivered to the same cell at
the same time the transient assay was performed using only Level 2 vectors (Figure 8). 2) we
had harvested the plants only 3 days after the transient assay as described in the literature
(Sparkes et al., 2006; Nekrasov et al., 2013). Although that´s the time with the peak of protein
expression (Sparkes et al., 2006; Nekrasov et al., 2013), perhaps the protein may not have
enough time to induce the mutation since in CRISPR/Cas9 genome editing we have to way until
the protein Cas9 to edit the genome. That is why we changed the harvest time from 3 days to 6
days (Figure 13), to provide enough time for genome editing. 3) we performed the RE assay
after the PCR, while it would have been more efficient to enrich the genomic DNA, for this
reason, we changed the restriction enzyme site loss method assay (RE): the genomic DNA was
digested before the first PCR, in order to enrich the genomic DNA with the edited sequences.
The enzymes used for each sgRNA are described in Table 10.
Figure 13. Transient assay – 6 days after Agro-infiltration; the plants were incubated for 2
days in the dark and for 4 days under a 12:12 dark: light photoperiod.
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After these changes, we could detect which sgRNA was working and sent them for
sequencing to detect mutations (Figure 14 B, Figure 15 B). For HCT, only the construct
targeting group 2 (haplotype HCT 1 and 2) worked properly (Figure 14). We sequenced the
PCR samples to confirm the genome editing, in all sequences analyzed we obtained a mixture
of sequences after the seed region indicating the presence cell with mutation and cell without
mutations (Figure 14 B). Considering that the HCT haplotype 1 was previously characterized
as important for lignin biosynthesis (Hoffmann et al., 2004), the production of stably edited
plants will be performed with these sgRNAs. Probably, the use of sgRNA that target only one
specific group of haplotypes can overcome the dwarf phenotype found previously by Hoffmann
et al., (2004).
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Figure 14. Results from Agro-transient assay with sgRNA from HCT. A) PCR from Agro-
transient assay with the sgRNA from HCT after digestion with the enzymes Hpy166 II and
BseLI, the figure shows which sample was digested with the enzymes. The first line is referent
from PCR using primers from HCT group 1 – Samples number 1 – 13 and 27 – 37 were
amplified with primers to target HCT 4; while samples numbers 14 – 26 and 38 – 48 were
amplified with primers to target HCT 3. M - NEB 2-log; 1 – WT DNAg digested with Hpy166II;
2 – 7 DNAg from TE using Construction A Replicate 1 – 6 digested with Hpy166 II; 7 – 11
DNAg from TE using Construction B Replicate 1 – 4 digested with Hpy166 II; 12 – WT DNAg
without digestion; 13 – Blank; 14 – WT DNAg digested with Hpy166II; 15 – 20 DNAg from
TE using Construction A Replicate 1 – 6 digested with Hpy166 II; 21 – 24 DNAg from TE
using Construction B Replicate 1 – 4 digested with Hpy166 II; 25 – WT DNAg without
digestion; 26 – Blank; 27 – WT DNAg digested with BseLI; 28 – 33 DNAg from TE using
Construction A Replicate 1 – 6 digested with BseLI; 34 – 37 DNAg from TE using Construction
B Replicate 1 – 4 digested with BseLI; 38 – WT DNAg digested with BseLI; 39 – 44 DNAg
from TE using Construction A Replicate 1 – 6 digested with BseLI; 45 – 48 DNAg from TE
using Construction B Replicate 1 – 4 digested with BseLI;. The second line is referent from
PCR using primers from HCT group 2 – Samples number 49 – 61 amplified with primers to
target HCT1; while samples numbers 62 – 74 to target HCT 2. M - NEB 2-log; 49 – Blank; 50
– WT DNAg; 51 – WT DNAg digested with Hpy166II; 52 – 57 DNAg from TE using
Construction A Replicate 1 – 6 digested with Hpy166 II; 58 – 61 DNAg from TE using
Construction B Replicate 1 – 4 digested with Hpy166 II; 62 – WT DNAg digested with Hpy166
II; 63 – 68 DNAg from TE using Construction A Replicate 1 – 6 digested with Hpy166 II; 69
– 72 DNAg from TE using Construction B Replicate 1 – 4 digested with Hpy166 II 73 – WT
DNAg; 74 – Blank; B) PCR sequencing to detect mutations – in the first line the WT sequence
(pointed in red) and below three sequences from hct-crispr mutated sequences (pointed in blue).
All CSE constructs worked as expected (Figure 15 A). The sequencing confirmed
the presence of mutations, both CSE1 and CSE2 were mutated to CRISPR/Cas9. While for
CSE1 we obtained deletions for CSE2 we obtained a chromatogram with a mixture of sequences
100
after the seed region indicating the presence of cell with different sequences (Figure 15 B) but
in both cases, the mutations were enough to change the frame of the protein generated. Both
constructs for CCoAOMT4 worked properly (Figure 16).
Figure 15. Results from Agro-transient assay with sgRNA from CSE. A) PCR from Agro-
transient assay with the sgRNA from CSE after digestion with the enzymes AgeI and BceAI,
the figure shows which sample was digested with the enzymes. The first line is referent from
PCR using primers from CSE1. M - NEB 2-log; 1 – Blank; 2 – 5 DNAg from TE using
Construction F Replicate 1 – 4 digested with AgeI; 6 – WT DNAg digested with AgeI; 7 – WT
DNAg without digestion; 8 – WT DNAg digested with BceAI; 9 – 12 DNAg from TE using
Construction F Replicate 1 – 4 digested with BceAI. The second line represents the PCR using
the primers from CSE2. M - NEB 2-log; 13 – Blank; 14 – WT DNAg without digestion; 15 –
WT DNAg digested with AgeI; 16 – 19 DNAg from TE using Construction F Replicate 1 – 4
digested with AgeI; 20 – WT DNAg digested with BceAI; 21 – 24 DNAg from TE using
Construction F Replicate 1 – 4 digested with BceAI. B) PCR sequencing to detect mutations –
Pointed in blue the sequences from CSE 1- in the first line the WT sequence from CSE1 and
the two lines below are two sequences from cse1-crispr mutated sequences; pointed in red the
sequences from CSE2 – in the first line WT sequences and below two sequences from cse2-
crispr mutated sequences.
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Figure 16. PCR from Agro-transient assay with the sgRNA from CCoAOMT4 after
digestion with the enzymes Hpy166 II. M - NEB 2-log; 1 – Blank; 2 – WT DNAg without
digestion; 3 – WT DNAG digested with Hpy166 II; 4 – 7 DNAg from TE using Construction I
Replicate 1 – 4 digested with Hpy166 II; 8 – 11 DNAg from TE using Construction J Replicate
1 – 4 digested with Hpy166 II.
4. Discussion
In the present study, we achieved the targeted mutagenesis of three key genes from
lignin (CSE, HCT, and CCoAOMT) metabolism in tobacco using CRISPR/Cas9 system by
agrotransient expression. This achievement opens the possibility for the development of
tobacco stable transformation to genome edit lignin/CGA pathway. The CRISPR/Cas9 system
has been previously used to target gene from lignin metabolism as 4-coumarate:coenzyme A
(4CL), involved in early steps of the lignin pathway, in poplar and in switchgrass (Zhou et al.,
2015; Park et al., 2017). Recently, Takeda et al., (2019) target conipheraldehyde 5-hydrolase
(OsCAld5H1) in rice. All these studies showed a high level of accuracy and reproducibility in
the results to study lignin metabolism. In tobacco, previous studies have shown the use of
CRISPR/Cas9 to target marker genes either in protoplast or in stable plants (Jiang et al., 2013;
Li et al., 2013; Nekrasov et al., 2013; Gao et al., 2015) but none of them to target gene from
lignin metabolism.
Here, we used database and online designs tools to find the best sgRNA to CSE and
CCoAOMT genes, these online tools found the best seed sequence near PAM, already
considering potential off-targets. On the other hand, to HCT we designed sgRNA manually
since our goal was to distinguish between the four alleles found in the tobacco genome.
Comparing to conventional downregulation by transgenic approach (RNAi, amiRNA),
CRISPR/Cas9 has the advantage to enable transgene segregation and, therefore, eliminate the
effect of transgene insertion (Xu et al., 2015; Zhou et al., 2015). To design sgRNA to HCT, we
gather the alleles in two groups based on their similarities: one group closer from the ancestors
N. sylvestris (2n=24) and the other closer from N. tomentosiformis (2n=24). Tobacco
(N.tabacum) genome is allotetraploid (2n=4x=48) (Leitch et al., 2008) and, for this reason, the
design of specific sgRNA was more challenging, especially considering our goal of targeting
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only specific haplotypes. Thus, to achieve this goal, we eliminated the regions where the
sequences were similar to design the sgRNA manually in a region where PAM was present for
the alleles of interest and absent for the other two alleles. The allele-specific targeting has been
previously reported for genome editing of animal and human genome due to its applicability in
genic therapy (Yoshimi et al., 2014; Shin et al., 2016). Shin et al., (2016) developed a
CRISPR/Cas9 strategy using two sgRNA to increase specificity in human cells. The dual gRNA
approach, using PAM altering SNPs, improved allele discrimination. The same strategy was
used in rats by Yoshimi et al., (2014). The group used a combination of sgRNA, one targeting
the mutant’s allele of interest and the other targeting the wild-type allele. This way, it was
possible to correct only the disease-associated phenotypes in rats. Here we used a combination
of two sgRNAs (i.e., both specific to the alleles we wanted to target) in order to create a deletion.
To assemble the constructs containing sgRNAs, Cas9 and Gene Marker we used
Golden Gate MoCLo toolkit. We selected this methodology because it is the most used for
multiplex assembly to enable simultaneous assemble of multiples sgRNAs (for a review see
Volpi e Silva and Patron, 2017). Using this strategy, we designed vectors to enable us
successfully genome-edited CSE, CCoAOMT and HCT group 2 (HCT1 and HCT2) alleles. The
RE-PCR was sufficient to identify the sequence with mutation in HCT1 and HCT2 since the
sgRNAs were designed to induce a 100bp deletion (Figure 14 A). Considering we used
agrotransient assay to induce mutation it was expected to obtain a mixture of cells, some of
them with mutations. For this reason, we identified the presence of two bands in the agarose
gel, one representing the sequences without mutation and/or with punctual mutations and
another one (100bp smaller) with the deletion expected. In accordance with this data, our
sequencing showed the presence of mutation and deletions in the expected region (Figure 14
B). Therefore, we successfully achieved our goal for this gene, which was to create a construct
capable of targeting one group of HCT alleles. This construct enables us to evaluate HCT impact
in lignin biosynthesis without interfering in plant development. Furthermore, we successfully
targeted both alleles of CSE using a combination of two sgRNA (Figure 15 A and B). Both
alleles were sequenced, and we observed the presence of mutations in both CSE1 and CSE2
(Figure 15 B). The construct aimed to induce mutations in CCoAOMT4 also worked as
expected, the RE-PCR showed the presence of two bands: one indicating deletions and another
one with the WT sequence (Figure 16). RE-PCR based method is a common strategy to detect
CRISPR/Cas9 mutations, since the use of RE can enrich DNA and facilitate the mutation
detection via PCR (Nekrasov et al., 2013; Lor et al., 2014; Wang et al., 2014b; Lawrenson et
al., 2015). In N. benthamiana. this method was used to validate CRISPR/Cas9 efficiency via
103
agro-transient expression assay (Nekrasov et al., 2013). Lawrenson et al., (2015) also used this
methodology in barley and B. oleraceae to screen stable plant transformants and find the
expected crispr/cas9-mutations. This way, the methodology used here to identify
CRISPR/Cas9 mutations in the agro-transient assay can also be applied in the screening of
tobacco stable plants using the constructions we designed.
In conclusion, we successfully designed and tested sgRNAs for HCT, CSE, and
CCoAOMT and induce mutations by transient expression in tobacco leaves using
CRISPR/Cas9. Our next step is to create stable edited plants and evaluate whether the resulting
loss-of-function mutants show any effect in lignin and/or CGA biosynthesis.
Acknowledgments
NVS thanks São Paulo Research Foundation for a BEPE doctoral fellowship (Processo
FAPESP n°2016/15834-2). This study was financed in part by the Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.
104
FINAL CONCLUSION
In conclusion, Chapter 1 was reviewed and discussed biochemical and molecular
evidence of the metabolic re-routing of CGAs towards lignin giving more support to data found
in Chapter 2. We developed transgenic tobacco plants by traditional agrobacterium
methodology for 7 different constructs and analyzed phenotypical, histochemical, biochemical
and molecular the transgenic from the construction HCTamiCSE, CSE and amiCSE –
overexpressing HCT and silencing CSE; only overexpressing CSE and only silencing CSE,
respectively. Our qPCR analyses from different organs from WT plants indicates that there is
no competition for the common intermediates between CGA and lignin biosynthesis. CSE
showed a pattern of expression that suggests its involvement in both pathways. The abundance
of HCT in stem seems to favor lignin branch, probably due to its low affinity to convert caffeoyl
CoA into CGA. On the other hand, in leaves, where there is an abundance of HQT, the higher
affinity from this enzyme to quinate seems to favor CGA biosynthesis. To support this
hypothesis, our CSE mutants had the excess of carbon generated by overexpression of CSE
remobilized into CGA in leaves but not in stem. These differences in carbon remobilization
observed between leaves and stem might be part of a strategy to finely regulate lignin branch
since the excess of caffeic acid can inhibit lignin route. The development of cse mutants
indicates CSE is essential for plant development since plants were severely dwarfed, possibly
due to the collapse of vascular vessels. The downregulation of CSE leads to a reduction of HQT
transcript level, reinforcing the interconnection between both pathways. Interestingly, the
overexpression of HCT in plants downregulating CSE recover the dwarfed phenotype
completely indicating that HCT is capable to produce caffeoyl CoA from caffeoyl shikimate.
This fact is remarkable since it is the first time that the ability of HCT to convert caffeoyl
shikimate was proved in planta. Moreover, comparing both mutants we found an opposite
pattern of cellulose content which might be associated with the differences in saccharification
efficiency found in these plants. Even though HCT and CSE are part of the same branch in
phenolic pathway they have different roles in plant metabolism and trigger different responses
upon perturbation. Lignin metabolic flux seems to be finely regulated in tobacco, since
overexpression of shikimic branch does not affect drastically lignin content. The accumulation
of caffeic acid in both mutants is an indicator that this compound might be a key point to
regulate lignin pathway in tobacco. In Chapter 3 we developed constructs to genome edit
tobacco by CRISPR/Cas9 and tested it by agro-transient assay. Furthermore, to help us to clarify
the data found in Chapter 2 we also developed constructs to partially silence the gene HCT to
105
try to overcome the dwarfed phenotype previously described and designed a construct to target
CCoAOMT4 – the isoform with higher level of expression in stem and the next step the pathway
after Caffeoyl CoA formation by HCT or CSE. All these constructs were tested by agro-
transient assay in tobacco and we were successful in generating mutations. The development of
these constructs enables us to generate mutated plants more specifically and with more accurate
results to understand lignin/CGA connection. We were able to develop 8 stable plants with HCT
CRISPR/Cas9 constructs, but these plants did not show any mutation, probably due to the low
number of plants analyzed. To summarize, our data indicate that CSE is main role of production
of Caffeoyl CoA in shikimate branch since its downregulation severely affect plant
development. Moreover, CSE overproduction favor CGA pathway, indicating a role of
shipmate branch in the production of CGA in tobacco. On the other hand, HCT is critical for
lignin metabolism and its overexpression associated with cse downregulation does not seem to
affect CGA pool (i.e., probably due its low affinity to quinate). Our work also indicates that
HCT is capable to produce caffeoyl CoA even though it is probably not the main route of
production.
106
PERSPECTIVES
Our next step to finalize Chapter 2 is to analyse by qPCR other genes from lignin
pathway to see how the route was affected. In addition to that, a biochemical analysis from cse
mutants would help us to better understand the dwarfed phenotype we observed. In order to
finalize Chapter 3 we will develop stable mutants with the constructs we develop, in this step
we will be able to obtain transgene free plant in order to analyse the mutations in HCT, CSE
and CCoAOMT genes with more accuracy.
107
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SUPPLEMENTARY INFORMATION
Supplementary Table 1. The matrix of identity from HCT isoforms.
Sequence HCT1 HCT2 HCT3 HCT4
HCT1 - 0.956 0.814 0.813
HCT2 0.956 - 0.823 0.821
HCT3 0.814 0.823 - 0.954
HCT4 0.813 0.821 0.954 -
125
Supplementary Table 2. BlastN from HCT in N.tabacum TN90 Genome in Sol Genomics
database.
Description e-
value Ident Region mRNA Name
Ntab-TN90_AYMY-
SS16269 0 100%
396784-397686, 401711-
402118
gene_27881 HCT1
Ntab-TN90_AYMY-SS218 0 96% 92884-93786, 89665-90072 gene_45849 HCT2
Ntab-TN90_AYMY-
SS16452 0 83%
476756-477647, 481209-
481619
gene_29243 HCT3
Ntab-TN90_AYMY-SS9042 0 83% 50610-51501, 48086-48496 gene_83292 HCT4
Ntab-TN90_AYMY-
SS10287 1e-127 91%
522424-522775 -
126
Supplementary Table 3. BlastP to search CSE from tobacco in SOL genomics database.
ID Sol Genomics ID Name Query
Cover
e-
value Ident ID
Name
mRNA_119258_cds Lysophospholipase
2 89%
e-
153 80%
AT1G52760
.1
CSE2
mRNA_108581_cds Lysophospholipase
2 88%
e-
153 80%
AT1G52760
.1
CSE1
127
Supplementary Table 4. BlastN from CSE in Sol Genomics database – N. tabacum TN90
Genome.
ID Sol Genomics Sequence Cromossome Localization
Ntab-TN90_AYMY-SS390 mRNA_119258 - gene_55941
(CSE2)
13652-14489, 22395-22962
Ntab-TN90_AYMY-
SS2876
mRNA_108581 - gene_50887
(CSE1)
200285-201004, 198409-
198999
128
Supplementary Figure 1. Example of age and size from tobacco plants used in the
analyses. A) Region was the histological cuts were done – 7th internode; B) Height from plants
used for the analyses.
129
Supplementary Figure 2. qPCR from CSE Mutants and HCTamiCSE double-mutants in
T0 in order to select the lines to analyze in T1 generation. A) qPCR from CSE gene in T0
CSE mutants; B) qPCR from CSE gene in T0 HCTamiCSE double mutants; C) qPCR from
HCT1 gene in T0 HCTamiCSE double mutants.
0
10
20
30
40
50
60
70C
SE
1 P
1
CS
E1 P
2
CS
E1 P
3
CS
E1 P
4
CS
E1 P
6
CS
E1 P
7
CS
E1 P
8
CS
E1 P
9
CS
E1 P
12
CS
E2 P
1
CS
E2 P
2
CS
E2 P
3
CS
E2 P
5
CS
E2 P
6
CS
E2 P
8
CS
E2 P
9
CS
E2 P
12
CS
E2 P
14
WT
Rela
tive
Qu
an
titi
es
(CN
RQ
) qPCR CSE - CSE mutants T0
0
1
2
3
4
5
6
Re
lati
ve
Qu
an
titi
es
(CN
RQ
) qPCR CSE - HCTamiCSE mutants T0
0
5
10
15
20
Re
lati
ve
Qu
an
titi
es
(CN
RQ
) qPCR HCT - HCTamiCSE mutants T0
A
B
C
130
Supplementary Table 5. Matrix of identity between the haplotypes of HCT from N.tabacum
and the haplotypes found in the genomes of the ancestors. The number highlighted in red
represent the closest ones. Gene N.sylv.
gene_31158
N. tomen
gene_29186
N.tomen
gene_21152
HCT4 HCT1 HCT3 HCT2
N.sylv
gene_31158
0.0000 0.0464 0.2056 0.0480 0.2068 0.0008 0.1949
N.tom
gene_29186
0.0464 0.0000 0.2045 0.0015 0.2057 0.0456 0.1951
N.tom
gene_21152
0.2056 0.2045 0.0000 0.2066 0.0008 0.2052 0.0457
HCT4 0.0480 0.0015 0.2066 0.0000 0.2078 0.0473 0.1972
HCT1 0.2068 0.2057 0.0008 0.2078 0.0000 0.2063 0.0449
HCT3 0.0008 0.0456 0.2052 0.0473 0.2063 0.0000 0.1944
HCT2 0.1949 0.1951 0.0457 0.1972 0.0449 0.1944 0.0000
131
Supplementary Figure 3. qPCR from tobacco stem and leaves to compare the expression
from both groups of HCT alleles.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
HCT Group 2 HCT Group 1
Re
lati
ve
qu
an
tite
CN
RQ
Groups of HCT haplotypes
Stem Leaves
132
Supplementary Figure 4. Second round of PCR after BseLI digestion from PCR of agro-
transient assay using Level 1 vectors. M - NEB 2-log; 2 – negative control; 3 – Digestion
from genomic DNAg used as control (HCT1); 4 – 7 PCR after digestion of samples 9 -12
from Figure 11 (HCT1);8 – Digestion from genomic DNAg used as control (HCT2); 9 – 12
PCR after digestion of samples 13 – 16 from Figure 11 (HCT2).
M 1 2 3 4 5 6 7 8 9 10 11 12
133
Supplementary Table 6. Experiment design to assemble Level 2 vectors.
Name Level 1
Position1
Level
1
Position2
Level 1
Position3
Level 1
Position4
Level 1
Position5
Level 1
Position6
Level 1
Position
7
A NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
1.1
U6::26sgRN
A1.6
U6-
26::sgRNA
2.6
U6-
26::sgRNA2
.3
pICH418
22
B NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
1.4
U6-
26::sgRNA1.
2
U6-
26::sgRNA
2.6
U6-
26::sgRNA2
.3
pICH418
22
C NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
1.1
U6-
26::sgRNA1.
6
pICH41780 - -
D NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
1.4
U6-
26::sgRNA1.
2
pICH41780 - -
E NOS::Kana
mycin
CaMV35
S:: Cas9 pICH54033 pICH54044
U6-
26::sgRNA
2.3
U6-
26::sgRNA2
.6
pICH418
22
F NOS::Kana
mycin
CaMV35
S:: Cas9 pICH54033
U6-
26::sgRNA4.
2
U6-
26::sgRNA
4.4
pICH41800 -
G NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
5.1
U6-
26::sgRNA
5.6
pICH41780 - -
H NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
5.7
U6-
26::sgRNA5.
5
pICH41780 - -
I NOS::Kana
mycin
CaMV35
S:: Cas9 pICH54033 pICH54044
U6-
26::sgRNA
8.1
U6-
26::sgRNA8
.4
pICH418
22
J NOS::Kana
mycin
CaMV35
S:: Cas9 pICH54033 pICH54044
U6-
26::sgRNA
8.1
U6-
26::sgRNA8
.5
pICH418
22
K NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
9.1
U6-
26::sgRNA
9.3
pICH41780 - -
L NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
9.2
U6-26::
sgRNA 9.3 pICH41780 - -
M NOS::Kana
mycin
CaMV35
S:: Cas9 pICH54033 pICH54044
U6-
26::sgRNA
10.1
U6-
26::sgRNA1
0.2
pICH418
22
N NOS::Kana
mycin
CaMV35
S:: Cas9
U6-
26::sgRNA
11.1
U6-
26::sgRNA
11.2
pICH41780 - -
134
Supplementary Table 7. Experiment design to Agro-infiltration. The table describes the
gene/group of genes targeted, which sgRNA combination was used, the expected size of
deletion, the enzyme used to the restriction enzyme (RE) site loss method and the target from
RE.
Group
Name Gene sgRNA sgRNA
Size
Deletion
Restriction
Enzyme
sgRNA
Targeted
RE
TE 1 HCT
group1 1.1 1.2 20pb Hpy166II Deletion
TE 2 HCT
group1 1.4 1.6 37pb None -
TE 3 HCT
group2 2.2 2.3 60pb BseLI 2.3
TE 4 HCT
group2 2.6 2.7 29pb HincII 2.6
TE 5 CSE 4.1 4.2 30pb BceAI Deletion
TE 6 CSE 4.1 4.5 100pb NcoI 4.5
TE 7 CSE 4.2 4.4 76pb AgeI 4.4
135
ATTACHMENT
Attachment 1
136
Attachment 2
137
Attachment 3
