© The Authors, 2021, Published by the Universidad del Zulia*Corresponding author: lgonzalezpaz@gmail.com
Design and characterization of sgRNAs aimed at the control of the phytopathogen
Pseudocercospora jiensis that causes Black Sigatoka
Diseño y caracterización de sgRNAs dirigidos al control del topatógeno Pseudocercospora jiensis
causante de la Sigatoka Negra
Projeto e caracterização de sgRNAs visando o controle do topatógeno Pseudocercospora jiensis
causador da Sigatoka Negra
Luis Moncayo
1
Paulo Centanaro
2
Diego Arcos-Jácome
2,8
Alex Castro
2
Cristina Maldonado
3
Diego Vaca
4,8
Gardenia González
8
Carla Lossada
5
Aleivi Perez
6
Lenin González-Paz
7,8*
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223909
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v39.n1.09
Crop Production
Associate editor: Dr. Jorge Vilchez-Perozo
Abstract
Black Sigatoka, caused by the fungus Pseudocercospora jiensis
(Mycosphaerella jiensis) is an important disease of bananas and plantain.
The design of sgRNAs molecules for gene silencing offers the possible control
of this phytopathogen. The sgRNAs, are molecules that bind to enzymes
to specically edit genes of interest. The use of these molecules requires
the use of bioinformatics tools for their study. Therefore, the objective of
this research was to design and characterize sgRNAs to silence the Fus3
virulence gene and CYP51 gene growth in P. jiensis, through the analysis
of structural, thermodynamic and functional characteristics that allow
to discriminate the sgRNAs candidates for control of the phytopathogen.
Several thermodynamically stable sgRNAs with high specicity for the target
genes were achieved, as well as with sequences easily recognizable by the
SpCas9 nuclease, and with sizes that allow efcient diffusion in eukaryotic
cytoplasms. The results suggest that all the designed and characterized
sgRNAs can promote the correct silencing of the genes selected for the
control of P. jiensis. Additionally, the most optimal designs were identied,
based on the characteristics considered in this study. These results, although
they require additional studies to improve the technology, are promising as
they show the possibility of using non-toxic and highly specic molecular
tools in plant biotechnology for genetic improvement, directed mutagenesis,
plant sanitation and control of phytopathogens.
Keywords:
CRISPR-Cas9
Banana
Crowding
Thermodynamics
1
Universidad Católica de Cuenca, Ecuador.
2
Universidad Agraria de Ecuador, Facultad de Ciencias Agrarias,
Ecuador
3
Laboratorio
de
Biotecnología
de
la
Facultad
de
Ciencias
Agropecuarias, Universidad Técnica de Babahoyo, Ecuador.
4
Universidad Laica Eloy Alfaro de Manabí, Ecuador.
5
Laboratorio
de
Caracterización
Molecular
y
Biomolecular
–
Centro
de
Investigación
y Tecnología
de
Materiales
-
Sección
Microbiología
Molecular
y
Biofísica.
Instituto
Venezolano
de
Investigaciones Cientícas - Zulia. Maracaibo, Venezuela
6
Laboratorio
de
Microbiología
General,
FEC-LUZ.
Zulia,
Maracaibo, Venezuela
7
Laboratorio
de
Genética
y
Biología
Molecular,
FEC-LUZ.
Zulia, Maracaibo, Venezuela.
526
8
Doctorado
en
Ciencias
Agrarias,
Facultad
de
Agronomía,
Universidad del Zulia, Zulia, Venezuela.
Received:
01-10-2021
Accepted:
08-11-2021
Published: 03-01-2022
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223909. January - March. ISSN 2477-9407.
2-6 |
Resumen
La Sigatoka Negra, causada por el hongo Pseudocercospora
jiensis (Mycosphaerella jiensis) es una enfermedad importante
del banano y plátano. El diseño de moléculas sgRNAs para el
silenciamiento de genes ofrece un posible control de este topatógeno.
Los sgRNAs son moléculas que se unen a enzimas para cortar de forma
especíca genes de interés. El aprovechamiento de estas moléculas
requiere usar herramientas bioinformáticas para su estudio. Por lo que
el objetivo de esta investigación fue diseñar y caracterizar sgRNAs
para silenciar el gen de virulencia Fus3 y el gen de crecimiento CYP51
en P. jiensis, mediante el análisis de características estructurales,
termodinámicas y funcionales que permiten discriminar los sgRNAs
candidatos a control del topatógeno. Se obtuvieron diversos
sgRNAs termodinámicamente estables y con alta especicidad para
los genes diana, así como con secuencias fácilmente reconocibles
por la nucleasa SpCas9, y con tamaños que permiten la difusión
eciente en citoplasmas eucariotas. Los resultados sugieren que
todos los sgRNAs diseñados y caracterizados, pueden promover el
correcto silenciamiento de los genes seleccionados para el control de
P. jiensis. Adicionalmente, se identicaron los diseños más óptimos,
en función de las características consideradas en este estudio. Estos
resultados, aunque requieren de estudios adicionales para perfeccionar
la tecnología, son prometedores pues muestran la posibilidad de
usar herramientas moleculares no tóxicas y de alta especicidad en
biotecnología vegetal para el mejoramiento genético, mutagénesis
dirigida, saneamiento vegetal y control de topatógenos.
Palabras clave: CRISPR-Cas9, banano, crowding, termodinámica.
Resumo
A Sigatoka Negra, causada pelo fungo Pseudocercospora
jiensis (Mycosphaerella jiensis), é uma doença importante da
banana e da banana-da-terra. O desenho de moléculas de sgRNAs
para silenciamento de genes oferece um possível controle deste
topatógeno. sgRNAs são moléculas que se ligam a enzimas para
cortar especicamente genes de interesse. O uso dessas moléculas
requer o uso de ferramentas de bioinformática para seu estudo.
Portanto, o objetivo desta pesquisa foi projetar e caracterizar sgRNAs
para silenciar o gene de virulência Fus3 e o gene de crescimento
CYP51 em P. jiensis, por meio da análise de características
estruturais, termodinâmicas e funcionais que permitem discriminar
sgRNAs candidatos para controlar topatógenos. Vários sgRNAs
termodinamicamente estáveis foram obtidos com alta especicidade
para os genes alvo, bem como sequências facilmente reconhecíveis
pela nuclease SpCas9, e com tamanhos que permitem difusão
eciente em citoplasmas eucarióticos. Os resultados sugerem que
todos os sgRNAs projetados e caracterizados podem promover o
correto silenciamento dos genes selecionados para o controle de P.
jiensis. Além disso, os designs mais ideais foram identicados, com
base nas características consideradas neste estudo. Esses resultados,
embora necessitem de estudos adicionais para o aprimoramento da
tecnologia, são promissores, pois mostram a possibilidade do uso
de ferramentas moleculares atóxicas e altamente especícas em
biotecnologia vegetal para melhoramento genético, mutagênese
dirigida, saneamento vegetal e controle de topatógenos.
Palavras chave: CRISPR-Cas9, banana, aglomeração,
termodinâmica.
Introduction
Black Sigatoka, caused by the fungus Pseudocercospora jiensis
(Mycosphaerella jiensis) is one of the most important diseases
of banana and plantain from the economic point of view, causing
a decrease of more than 40% of the yield (Díaz-Trujillo et al.,
2018). The control of the disease is carried out mainly through the
extensive application of chemical fungicides, which causes a negative
environmental impact (Escobar-Tovar et al., 2015).
Interestingly, a genetic system applicable to control pathogens
has been described in bacteria and is based on the use of non-coding
RNA molecules or simple RNA guides (sgRNAs, single guide RNAs)
that direct nucleases of the CAS family, such as Cas9, encoded by
genes associated with CRISPR (clustered regularly interspaced
short palindromic repeats) or cas genes, to form a ribonucleoprotein
complex (RNP) made up of Cas9-sgRNA that specically cuts
genetic material thanks to the recognition of sequences called PAM
(protospacer adjacent motif) (Kocak et al., 2019). This system
has been proposed as a tool for programmable gene and genome
editing (Campenhout et al., 2019). This has created experimental
opportunities and ethical challenges since the technique can produce
off-target cuts (off-target events) (Bartkowski et al., 2018). However,
the study of CRISPR/Cas9 systems is important because they are
precursors of next generation tools for genetic manipulation (Belhaj
et al., 2015).
The sgRNAs can be designed to inactivate genes of importance
in pathogens. For example, the CYP51 gene encodes for cytochrome
P450 14α-demethylase, a key enzyme in the biosynthesis of ergosterol
in M. jiensis
Morelet, so its inhibition can prevent the formation of
cell membranes and the growth of fungi (Ma and Tredway, 2013).
El gen Pfcyp51 de P. jiensis ha sido asociado a la resistencia a los
inhibidores de 14α-desmetilasa (DMI), de modo que, su silenciamiento
contribuye a la recuperación de la sensibilidad a DMI (Díaz-Trujillo
et al., 2018; Chong et al., 2019). The Fus3 gene regulates invasion
pathways such as the formation of infection, sporulation, invasive
and lamentous growth structures of phytopathogenic fungi (Onyilo
et al., 2018). The Fus3 gene of P. jiensis has been experimentally
silenced, achieving reduced virulence, as well as decreased invasive
growth (Onyilo et al., 2018).
CRISPR-Cas9 technology has already been applied to silence
genes of banana pathogens (Tripathi et al., 2019). Likewise, it has
been used in plants for plant improvement purposes (Zaynab et al.,
2020). However, little progress has been made in P. jiensis (Escobar-
Tovar et al., 2015). Filamentous fungi such as P. jiensis are more
complex to genetically manipulate due to their morphology, cell
differentiation, membranes, and thick chitinous cell walls (Jiang
et al., 2013). Although some species have been manipulated, the
technology is still under development (Estrela and Cate, 2016). On
the other hand, RNAi has been proposed for the silencing of virulence
and growth genes in P. jiensis (Mumbanza et al., 2013) and in
related pathogens (Koch et al., 2013), and additionally, efforts have
been made to introduce CRISPR-Cas9 systems in various lamentous
fungi with promising results (Estrela and Cate, 2016)
To use these systems it is necessary to consider factors such as
possible cuts in off-target sites (Tripathi et al., 2019), thermodynamic
stability (Kocak et al., 2019) and even the size of said molecules
by the cytoplasmic congestion phenomenon (Dupuis et al., 2014).
The latter is a novel aspect little considered (Kocak et al., 2019).
Although all these parameters must be estimated for the validation
and optimization of sgRNAs designed for the study and control of P.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Moncayo et al. Rev. Fac. Agron. (LUZ). 2022, 39(1): e223909
3-6 |
jiensis (Escobar-Tovar et al., 2015; Díaz-Trujillo et al., 2018). In this
sense, a comparative analysis of designed sgRNAs targeting genes of
interest in P. jiensis was carried out, studying functional, structural
and biophysical aspects in order to characterize and discriminate
candidate sgRNAs for CRISPR-Cas9 tools.
Materials and methods
Data sources and Design of sgRNAs for the control of P.
jiensis
The genomic sequence used in this study for the design of
sgRNAs was obtained from DOE JGI (http://www.jgi.doe.gov)
and NCBI (http://www.ncbi.nlm.nih.gov/). Two target genes were
selected, Fus3 (Xu, 2000) and CYP51 (Podust et al., 2001), associated
with virulence and cell division in P. jiensis, respectively. For
the design, the CRISPOR program (http://crispor.tefor.net/) was
used, which includes the genome of P. jiensis CIRAD86-NCBI
GCF_000340215.1. Streptococcus pyogenes Cas9 (SpCas9) with
PAM recognition sequence: “NGG” in the 3 ‘sense was used as
nuclease (Campenhout et al., 2019).
Characterization of the designed sgRNAs
Each sgRNA, including the PAM motif, was compared to the P.
jiensis genome using DOE JGI’s BLASTN program (http://www.
jgi.doe.gov) to identify potential off-target sites. The results of the
DOE JGI will be compared with the possible non-destination sites
offered by the CRISPOR algorithm. The secondary structures of
sgRNA were predicted with RNA fold (http://rna.tbi.univie.ac.at/
cgi-bin/RNAWebSuite/barriers.cgi) as well as the number of possible
suboptimal structures by calculating the folding energy (K) of
RNAs using the barriers web server (http://rna.tbi.univie.ac.at/cgi-
bin/RNAWebSuite/barriers.cgi). The Gibbs minimum free energy
thermodynamic parameters for formation (∆G), entropy (∆S), enthalpy
(∆H) and melting temperature (Tm) were obtained with mfold (http://
unafold.rna.albany.edu/?q=mfold/RNA-Folding-Form2.3). In all
cases, the default parameters were considered. % GC, molecular mass
(MW) and regions of maximum exibility were determined using the
Unipro UGENE-v.1.32.0 software.
The sgRNA size was determined by calculating the solvent
accessible surface area with the Shrake-Rupley algorithm (SASA
method, solvent accessible surface area) (Dupuis et al., 2014). To
determine the diffusion coefcient (D) of sgRNAs in a model of high
and normal degree of crowding (Regan et al., 2018). The Stokes-
Sutherland-Einstein (SSE) equation was used, which establishes the
diffusion relationship between the viscosity of the cytoplasm, the
thermal energy and the size of the molecule. The coefcient D was
calculated using accepted cell models such as HeLa (cytoplasmic
viscosity of ≈ 4.4x10
-2
Pa.s
-1
a 37º C) and the Swiss 3T3 normal cell
model (viscosity of 2.4x10
-2
Pa.s
-1
a 37º C).
Complementary analysis
Simple and multiple correlation analyzes were applied (as the
case may be) to look for co-linearities in the variables considered,
and to be able to reduce them and / or propose predictive models.
The t test (Student’s) was also used to corroborate the difference in
the means of the sgRNA groups designed and treated together with
descriptive statistics. Discriminant multivariate analysis was also
used, after treating primary variables in search of orthogonality, in
order to have only predictor variables to classify the sgRNAs and
to be able to explain the membership of each designed molecule to
one or another pre-established design group. Multivariate normality
and homoscedasticity tests were applied. The Microsoft Excel 2010
application and the SPSS statistical package (IBM SPSS Statistics 23)
(George and Mallery, 2016) were used.
Results and discussion
Design of sgRNAs for the control of P. jiensis
From the possible guide sequences calculated for the Fus3 and
CYP51 genes, three of the best predicted sgRNAs were chosen
for each gene, taking into account those that presented the highest
CRISPOR score and that did not exhibit off-target. The sgRNAs were
designated as sgRNA-F1, F2 and F3; and sgRNA-C1, C2 and C3; for
sgRNAs designed for the Fus3 gene (designs F) and CYP51 (designs
C), respectively (gure 1). The specicity mediated by the lack of
off-target was corroborated after the use of the DOE JGI alignment
tools, nding that the sequences chosen as potential sgRNA do
not present homology with other sequences outside the regions of
interest, promoting only the cleavage of the sequences homologous
to sgRNAs designed from the P. jiensis genome.
Figure 1. Prediction of the secondary structure of the designed sgRNAs and
their energy folding kinetics. The A) sgRNA-F1, B) F2 and C) F3, and D)
sgRNA-C1, E) C2 and F) C3 are shown, for the sgRNAs designed for the
silencing of the Fus3 gene (designs F) and CYP51 (designs C), respectively.
As well as the Gibbs minimum free energy for its formation (∆G) in kcal.mol
-1
and the energy folding kinetics (plotted by tree diagrams indicative of the
number of possible sub-optimal minimum energy structures) next to each one
of the sgRNA molecules designed in this study. The bar with the colorimetric
gradient from 0 (blue) to 1 (red) at the bottom of each gure represents the
probability of the correct base pairing and position.
The predominant PAM sequences in the designs were “TGG” (3/6)
for the sgRNA-F2, C2 and C3, and “AGG” (2/6) in the sgRNA-F1
and C1, and only the sgRNA-F3 presented the PAM sequence “GGG”
(1/6) (table 1). The importance of designing sgRNA to inactivate
(knockout) the Fus3 and CYP51 genes for the control of P. jiensis
lies in the fact that this phytopathogenic fungus is the cause of Black
Sigatoka, one of the most signicant diseases of banana and plantain.
These genes are important for the virulence and growth of this
pathogen (Xu, 2000; Podust et al., 2001), at the same time that the
strategy of blocking genetic determinants has already been tested in
related organisms (Mumbanza et al., 2013; Escobar-Tovar et al., 2015;
Díaz-Trujillo et al., 2018). However, many sgRNAs can be inefcient
in achieving knockout as a result of genetic variability, which plays
an important role in the complementarity and/or recognition of the
target by sgRNAs (Scott and Zhang, 2017).
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223909. January - March. ISSN 2477-9407.
4-6 |
Table 1. Thermodynamic characteristics of sgRNAs directed at P. jiensis.
Design/Target PAM Off-target
ΔG
(kcal.mol
-1
)
ΔH
(kcal.mol
-1
)
ΔS
(cal.K
-1
*mol
-1
)
sgRNA-F15’-ATGGCCGACCCGTCGCTCGC-3’ AGG 0 - 1.7 - 51.5 - 160.5
sgRNA-F2 5’-TATTACCTGTCTGTATGACA-3’ TGG 0 - 4.2 - 64.8 - 195.3
sgRNA-F3 5’-TCTGCGCGAATAGACCTAGT-3’ GGG 0 - 1.5 - 52.4 - 164.1
sgRNA-C1 5’- CCGTGGTGCTGGGGACTAA-3’ AGG 0 - 3.9 - 65.8 - 199.5
sgRNA-C2 5’- GTGCGTGCACACAATAAGC-3’ TGG 0 - 1.4 - 36.2 - 112.2
sgRNA-C3 5’-CGTCGACCTCCCGCCTGCTA-3’ TGG 0 - 0.1 - 16.6 - 53.2
The thermodynamic characteristics of the sgRNAs designed for the control of P. jiensis are presented; sgRNA-F1, F2 and F3 (molecules designed for the silencing of the
Fus3 gene); sgRNA-C1, C2 and C3 (molecules designed for the silencing of the CYP51 gene); PAM: nucleotide sequence or motif recognized by nuclease to generate cuts;
Off-target: number of cuts outside the sequence of interest; ∆G: minimum Gibbs free energy for the formation of the structure; ∆H: enthalpy contribution to the formation
energy; ∆S: entropic contribution to the formation energy.
In this study, the three best designs offered by the CRISPOR
software were selected, to guarantee a greater probability of excision
of the gene of interest (Liang et al., 2017). Because one of the main
problems behind the design of sgRNA is represented by off-target, a
phenomenon product of genetic polymorphisms (Scott and Zhang,
2017), which causes various unwanted events (Zhang et al., 2015;
Tripathi et al., 2019). Therefore, as off-target sites are not found in
this study from the selected sgRNAs, we are in the presence of an
important result indicative that the CRISPOR algorithm is capable
of offering specic guides, a promising fact in the constant search
to reduce the off-target (Zhang et al., 2015). Much of the efciency
of the guides is given by the PAM sequences, as they are the specic
nuclease recognition regions. These sequences are diverse, however,
in this study, the pool of selected sequences corresponds to PAM
typical of the Cas9 enzyme, specically, sequences recognizable by
SpCas9 (Campenhout et al., 2019).
Characterization of the designed sgRNAs
sgRNA with secondary structures typical of CRISPR-
associated primary transcripts with “stem-loop” conformations
were predicted. These structures presented a ∆G with a mean in
terms of absolute value of -2.13 kcal.mol
-1
. The mean ∆G for the
Fus3 gene was -2.46 kcal.mol
-1
and for CYP51 it was -1.80 kcal.
mol
-1
. In the case of sgRNAs for the Fus3 gene, the minimum ΔG
was -1.50 kcal.mol
-1
(sgRNA-F3) and a maximum of -4.20 kcal.
mol
-1
(sgRNA-F2). While for the CYP51 gene, the minimum ∆G
was -0.10 kcal.mol
-1
(sgRNA-C3) and maximum -3.90 kcal.mol
-1
(sgRNA-C1). Therefore, all the sgRNAs presented a ΔG <0 (gure
1, table 1). No signicant difference (p> 0.05) was found between
the ΔG presented by the sgRNA groups studied.
The sgRNAs present very stable secondary structures, with ∆G
for their formation thermodynamically favored, as was reported
for similar native CRISPR-type structures (Li et al., 2017). The
thermodynamic stability of sgRNAs is a critical aspect to guarantee
the success of CRISPR systems (Kuan et al., 2017). It has been
shown that the thermodynamic stability of the “stem-loops” can
increase the specicity of the designs in several orders of magnitude
of the SpCas9, especially if the ∆G is between 0 and -10 kcal.mol
-1
,
range in which were distributed all the sgRNAs designed in this
study (Kocak et al., 2019). This stability depends on the length of the
stems, the longer the stem, the greater the stability of the structure
(Li et al., 2017). A characteristic shared by the sgRNAs analyzed,
and that explains why there was a high correlation between the size
expressed in r and ∆G of the sgRNAs, with a coefcient of 0.68.
The folding energy of each design determined that there is a
statistically signicant difference (p <0.05) between the means
of the possible sub-optimal structures predicted for the guides.
The sgRNAs presented a mean value of ≈128 possible minimum
energy structures. With a minimum of 41 possible conformations
for sgRNA-C3 and a maximum of 266 for sgRNA-F2. All the
guides directed to the Fus3 gene present between 140-266 possible
structures of minimum energy, with the sgRNA-F3 being the one
with the lowest probability of change (≈140), while for the CYP51
gene the range of the folding kinetics of its guides was between 41
to 80 possible structures (gure 1, table 1).
In relation to the thermodynamic parameters that contribute to
the ∆G, it was found that there is no statistically signicant difference
(p> 0.05) between the groups of sgRNA studied and that the mean in
terms of the absolute value of the entropic contribution (∆S) in the
designs it was -147.47 cal.K
-1
.mol
-1
, an enthalpic contribution (∆H)
of -47.88 kcal.mol
-1
and a melting temperature (Tm) of 49.45º C.
Specically, the mean ∆S for the Fus3 gene was -173.3 cal.K
-1
.mol
-1
,
with a ∆H of -56.23 kcal.mol
-1
and a Tm of 50.66º C. While the
thermodynamic contributions for the designs directed to the CYP51
gene were a ∆S = -121.63 cal.K
-1
.mol
-1
, ∆H = -39.53 kcal.mol
-1
and
a Tm = 48.23º C. The design that presented the greatest entropic
contribution for the Fus3 gene was the sgRNA-F2 with a ∆S =
-195.3 cal.K
-1
.mol
-1
, while for CYP51 it was the sgRNA-C1 with an
∆S = -199.5 cal.K
-1
.mol
-1
, however, all designs targeting these genes
presented ∆S> ∆H, with ∆S <0 and ∆H <0.
The mean Tm calculated for all designs was 49.45º C. The Tm
of the guides for the Fus3 gene was 50.66º C and for CYP51 it
was 48.23º C. The sgRNA-F2 and sgRNA-C1 were the designs
that presented the highest Tm, this being 58.4º C and 56.5º C,
respectively. It should be noted that all sgRNAs presented Tm> 45º
C, except for sgRNA-C3 (table 2). The thermodynamic stability
determined is a reection of enthalpic and entropic contributions
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Moncayo et al. Rev. Fac. Agron. (LUZ). 2022, 39(1): e223909
5-6 |
and melting temperatures, parameters that are strongly correlated
with coefcients between 0.85 and 1.00, and show that sgRNAs
present unimolecular folding with a diverse number of possible
variations. conformational at 37º C, depending on the nucleotide
sequences and according to the calculations of the energy folding
kinetics. As reported, the sgRNA melting temperature (Tm) seems
to have little impact on the activity of the guide, except in the
guides with very high Tm, which are slightly less active (Brendan
et al., 2020). Although designs such as sgRNA-C3 can achieve a
stable conformation faster compared to the rest of the guides, by
presenting only ≈41 conformations, it was determined that all
sgRNAs are structures assembled by exothermic processes, with
structural tension, rigidity, little rotation, vibration and movement
of electronic charge, aspects that favor stability and coupling with
Cas9 (Kocak et al., 2019). This explains the correlation between
all the thermodynamic parameters and the parameters associated
with the size (r) and dynamics (D) of the sgRNA, with correlation
coefcients of up to 0.90.
Table 2. Structural and functional characteristics of guide RNAs aimed at controlling P. jiensis.
Design/Target T
m
(°C) %GC MM r (Å)
D
HeLa
(µm
2
.s
-1
)
D
3T3
(µm
2
.s
-1
)
K
sgRNA-F1 5’-ATGGCCGACCCGTCGCTCGC-3’ 47.5 73.91 7167.53 3.94 1.31x10
-18
2.40x10
-18
164
sgRNA-F2 5’-TATTACCTGTCTGTATGACA-3’ 58.4 39.13 7185.62 3.80 1.36x10
-18
2.49x10
-18
266
sgRNA-F3 5’-TCTGCGCGAATAGACCTAGT-3’ 46.1 56.52 7245.63 3.94 1.31x10
-18
2.40x10
-18
140
sgRNA-C1 5’- CCGTGGTGCTGGGGACTAA-3’ 56.5 60.87 7310.67 3.80 1.36x10
-18
2.49x10
-18
79
sgRNA-C2 5’- GTGCGTGCACACAATAAGC-3’ 49.4 56.52 7254.64 2.74 1.89x10
-18
3.46x10
-18
80
sgRNA-C3 5’-CGTCGACCTCCCGCCTGCTA-3’ 38.8 69.57 7093.48 0.82 6.30x10
-18
1.15x10
-17
41
The structural and functional characteristics of the sgRNAs designed for the control of P. jiensis are shown; sgRNA-F1, F2 and F3 (molecules designed for the silencing
of the Fus3 gene); sgRNA-C1, C2 and C3 (molecules designed for the silencing of the CYP51 gene); Tm: melting temperature of guide RNAs; % GC: guanine and cytosine
contents; MM: molecular mass; r: radius of the RNA molecule; DHeLa: diffusion coefcient of guide RNA in a cytoplasm considered congested (high crowding) for this
study from the HeLa cell line; D3T3: diffusion coefcient of guide RNA in a cytoplasm considered normal (low crowding) for this study from the 3T3 cell line; K: number
of possible sub-optimal structures of minimum energy.
The mean GC content for the sgRNAs was 59.42%, with a
molecular weight of 7209.59Da. The GC content of the designs
directed to Fus3 presented a mean value of 56.52%, and a mean
molecular weight of 7199.59 Da. On the other hand, the designs
for CYP51 presented a %GC of 62.32 and a molecular weight of
7219.59 Da. Being the sgRNA-F1 and sgRNA-C3, the designs
with the highest %GC, while the highest molecular weight was
calculated from the sgRNA-F3 and sgRNA-C1 (table 2). A high GC
content is important because it stabilizes the RNA-DNA duplex and
destabilizes the off-targets (Ren et al., 2014).
The difference between the molecular weight of the designs
was ≈20 Da. No statistically signicant differences (p> 0.05) were
found in these determined parameters in the sgRNA groups studied,
and regions of maximum exibility were not predicted. These
results show that the use of bioinformatics tools makes possible the
selection of specic sgRNAs, thermodynamically stable and with
suitable biophysical and molecular parameters. Aspects associated
with editing specicity and efcacy (Kuan et al., 2017).
The sgRNAs presented an r with a mean value of 3.172 Å,
equivalent to 6.344 Å in diameter, without signicant differences
(p> 0.05). The smallest size was calculated for the sgRNAs directed
to CYP51 with a mean of r = 2.451 Å, while for Fus3 it was r =
3.892 Å. The smallest sgRNAs for each gene were sgRNA-F2 (r =
3.796 Å) and sgRNA-C3 (r = 0.821 Å) (table 2). The difference in
size between the groups (sgRNA targeting Fus3 versus CYP51) had
a mean value of just r = 1.441 Å (approximate radius of the water
atom). From the calculated sizes it was possible to determine that
the
D coefcient of the sgRNAs in the congested HeLa cytoplasm
(high crowding) is ≈ 2.255x10
-18
µm
2
.s
-1
and in the normal cytoplasm
3T3 (normal crowding) of D = ≈ 4.131x10
-18
µm
2
.s
-1
. With a
mean for the guides directed to the Fus3 gene of D = 1.328x10
-18
µm
2
.s
-1
in HeLa and a D = 2.433x10
-18
µm
2
.s
-1
in 3T3. On the other
hand, the values for the sgRNAs designed for CYP51 were D =
2.433x10
-18
µm
2
.s
-1
for HeLa and D = 5.828x10
-18
µm
2
.s
-1
for 3T3.
The guide with the best D coefcient in HeLa for the Fus3 gene
was specically the sgRNA-F2 with a D = 1.361x10
-18
µm
2
.s
-1
, and
for CYP51 the sgRNA-C3 (D = 6.300x10-18 µm
2
.s
-1
). While the
guides with the best D coefcients under 3T3 were sgRNA-F2 (D =
2.494x10
-18
µm
2
.s
-1
) and sgRNA-C3 (D = 1.153x10
-17
µm
2
.s
-1
) (table
2). Although no statistically signicant differences were found
(p> 0.05) between the calculated D coefcients, the sgRNA-C3
presented the best coefcients in both HeLa and 3T3. Knowing
the size of the guide molecules is important because it has been
described that cytoplasms can cause changes of up to ΔG ≈ −2.5
kcal.mol
-1
(Dupuis et al., 2014). A high correlation between r and D
was predicted in the sgRNAs, with a coefcient of 0.96. Therefore,
when obtaining sgRNAs with D coefcients in the order of ≈10-
18 µm
2
.s
-1
in HeLa, it can be inferred that the sgRNAs considered
exhibit a three-dimensional diffusion similar to Cas9 (Knight et al.,
2015).
The number of possible energetically feasible sub-optimal
conformations or structures (k) of each sgRNA, turned out to be the
independent variable that best allows to differentiate the groups of
designs in a signicant way (p <0.05). An increase in
k (above the
mean) will make it more likely that a sgRNA will obtain a positive
score and, thus, that it will conform to the sgRNA pattern typical of
designs targeting the Fus3 gene in a signicant way. Additionally,
the structure matrix generated by the multivalent analysis excluded
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223909. January - March. ISSN 2477-9407.
6-6 |
the rest of the variables for not providing relevant classicatory
information. It is recommended to apply this multivariate statistical
model with a greater diversity of sgRNAs, to obtain other
discriminant functions or to corroborate the weight of the function
predicted here.
Conclusions
Optimal sgRNAs could be designed and identied using
bioinformatic tools based on structural, thermodynamic and
functional characteristics. The methods used to improve the
efciency of sgRNAs point to sgRNA-F3 and sgRNA-C3 as the
molecules with the most optimal characteristics for the knockout
of Fus3 and CYP51 in P. jiensis. Likewise, the number of possible
conformations has an important predictive weight to differentiate
between suitable sgRNAs for P. jiensis. These results, although
preliminary and require more studies, are promising because they
show the possibility of using non-toxic alternatives for genetic
improvement, and specic control of plant diseases, as more
research is carried out.
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