Insights into Leptocybe invasa resistance in Eucalyptus: phenotyping, genotyping and in silico approaches

Insights da resistência de Eucalyptus a Leptocybe invasa: abordagens fenotípicas, genotípicas e in silico

Abstract

The gall wasp, Leptocybe invasa, poses a significant global threat to Eucalyptus cultivation, by causing substantial economic losses. The objective of this study was to differentiate between resistant and susceptible genotypes by morphological characteristics using image analysis based on the damage caused by the gall wasp. In addition, consensus sequences derived from transposable elements (TEs) and the genome of Eucalyptus spp. Were identified by in silico analysis. Furthermore, another objective was to discriminate Eucalyptus genotypes in response to Leptocybe invasa by conducting molecular analyses involving transposable elements and inter simple sequence markers. For image analysis, the GroundEye ® system was used to collect images of 60 leaves from six genotypes, three of which were resistant and three susceptible. Eucalyptus spp. sequences were obtained from the GenBank database by in silico analysis and pairwise alignments with TE sequences were conducted using BLASTN. Multiple sequence alignment was performed with Clustal Omega, followed by the identification of conserved regions in Jalview. A motif signature was generated using Weblogo. For molecular characterization using ISSR markers and TEs, samples of young leaves were obtained from a total of 80 Eucalyptus seedlings, of which 50 were classified as resistant and 30 as susceptible to L. invasa. It was possible to distinguish gall wasp susceptible and resistant genotypes by image analysis. In silico analysis enabled the identification of conserved regions in the Eucalyptus spp. genome, which were associated with proteins involved in secondary metabolite production, e.g., terpenes, which play a role in the response to L. invasa. The discrimination capacity of TEs and ISSR primers was demonstrated and bands were generated that could be used to identify resistant genotypes. However, increasing the number of markers required to discriminate genotypes in both cases is suggested.

Keywords:
BLAST; terpene; economical damage; Groundeye®

Resumo

A vespa-da-galha, Leptocybe invasa, representa uma ameaça global significativa para o cultivo de Eucalyptus, causando consideráveis prejuízos econômicos. O objetivo deste estudo foi diferenciar entre genótipos resistentes e suscetíveis por meio de características morfológicas, usando análise de imagem com base nos danos causados pela vespa-da-galha. Além disso, sequências de consenso derivadas de elementos transponíveis (TEs) e do genoma de Eucalyptus spp. foram identificadas por análise in silico. Outro objetivo foi discriminar genótipos de Eucalyptus em resposta a L. invasa, por meio de análises moleculares envolvendo elementos transponíveis e marcadores de sequências simples intercaladas (ISSR). Para a análise de imagem, o sistema GroundEye® foi utilizado para coletar imagens de 60 folhas de seis genótipos, sendo três resistentes e três suscetíveis. Sequências de Eucalyptus spp. foram obtidas do banco de dados GenBank por análise in silico, e alinhamentos de sequências em pares com sequências de TE foram conduzidos usando o BLASTN. Alinhamento de múltiplas sequências foi realizado com Clustal Omega, seguido pela identificação de regiões conservadas no Jalview. Uma assinatura de motivo foi gerada usando o Weblogo. Para a caracterização molecular usando marcadores ISSR e TEs, amostras de folhas jovens foram obtidas de um total de 80 mudas de Eucalyptus, das quais 50 foram classificadas como resistentes e 30 como suscetíveis a L. invasa. Foi possível distinguir genótipos suscetíveis e resistentes à vespa-das-galhas por meio da análise de imagem. A análise in silico permitiu a identificação de regiões conservadas no genoma de Eucalyptus spp., associadas a proteínas envolvidas na produção de metabólitos secundários, como terpenos, que desempenham um papel na resposta a L. invasa. A capacidade de discriminação de TEs e iniciadores ISSR foi demonstrada, e bandas foram geradas que poderiam ser usadas para identificar genótipos resistentes. No entanto, sugere-se aumentar o número de marcadores necessários para discriminar genótipos em ambos os casos.

Palavras-chave:
BLAST; terpeno; danos econômicos; Groundeye®

1. Introduction

The gall wasp Leptocybe invasa represents a significant global threat to Eucalyptus trees, leading to substantial economic losses (Csóka et al., 2017; Le et al., 2018; Mendel et al., 2004). This pest primarily affects young plantations and causes devastating damage to seedlings in forest nurseries (Naidoo et al., 2018). The attack of L. invasa results in severe structural damage to Eucalyptus leaves, petioles and branches, affecting photosynthesis and forest yield (Carvalho et al., 2022; Rocha et al., 2023)​. Therefore, understanding this pest and establishing effective control strategies will be decisive to minimize productivity losses. Used in biological control, Quadrastichus mendeli (Hymenoptera: Eulophidae) was first recorded parasitizing L. invasa in Brazil in 2022, confirming the potential of this parasitoid as a biological control agent in integrated management (Puretz et al., 2022).

Studies that evaluated the response of Eucalyptus clones to L. invasa indicate that clones of certain pure species, including Eucalyptus moluccana, Eucalyptus urophylla, E. camaldulensis, Eucalyptus dunnii, Eucalyptus nitens, Eucalyptus grandis, as well as the hybrids E. urophylla × E. grandis, E. urophylla x Eucalyptus spp., Eucalyptus saligna × E. urophylla, can serve as sources of resistance to this pest ​(Dantas et al., 2021; Dittrich-Schröder et al., 2012; Eskiviski et al., 2018; Otieno et al., 2022)​. Dantas (2019) identified variations in the composition of secondary compounds, with a higher concentration of terpenes, among genotypes resistant and susceptible to the pest.

In integrated pest management, various measures can be adopted to deal with insects considered pests. This includes identifying the economic damage threshold, implementing cultural control measures, using biological and chemical control methods and utilizing resistant genotypes. In this regard, early evaluation of damage to vegetative structures can help identify the optimal timing for control by the use of chemical products. Tools that facilitate damage observation and monitoring, e.g., image analysis, are relevant to accelerate decision-making.

Genetic diversity plays a vital role in the adaptation and resilience of plant species to various stresses (Salgotra and Chauhan, 2023)​. The genetic composition of Eucalyptus genotypes can influence their ability to tolerate or resist L. invasa infestation (Mhoswa et al., 2022). Therefore, it is essential to evaluate the genetic distinction among Eucalyptus genotypes to identify resistant individuals that can contribute to breeding programs and the development of resistant clones.

To improve the understanding of genetic mechanisms in plants, a comprehensive analysis of genetic variants is necessary. The inbreeding level in the genus Eucalyptus is high. Multi-allelic markers, such as those provided by Transposable Elements (TEs), can be used as genetic markers to differentiate genotypes (Candotti et al., 2023; Wang et al., 2023). Transposable Elements are widely distributed in the Eucalyptus genome and influence various aspects of plant development and stress response. However, their specific involvement in the response to L. invasa in Eucalyptus remains unknown (Ferguson et al., 2023). Understanding the distinction of TEs between resistant and susceptible Eucalyptus plants can contribute to the selection of genotypes with desirable traits. Transposable Elements can also serve as potential molecular markers to measure genetic variability among different genotypes.

Inter-Simple Sequence Repeat (ISSR) markers have emerged as powerful tools for genetic diversity analysis (Sadeghpoor et al., 2023). They provide valuable information about the genetic structure and diversity within plant groups and are widely used in genetic studies due to their high variability and ability to detect polymorphisms (Shahabzadeh et al., 2020). ISSR markers are commonly used to discriminate genotypes ​(Ashraf et al., 2016; Basha and Sujatha, 2007; Costa et al., 2016; Kanbar, 2011; Sharma et al., 2009)​, facilitating the identification of genotypes of interest.

The objective of this study was to differentiate between resistant and susceptible genotypes based on image analysis of damage caused by the gall wasp. In addition, consensus sequences derived from transposable elements and the Eucalyptus spp. genome were identified by in silico analysis. Moreover, the Eucalyptus genotypes in response to Leptocybe invasa was discriminated by molecular analyses that involve TEs and ISSR markers.

2. Material and Methods

2.1. Plant material

A total of 150 Eucalyptus seedlings were provided by Bracell Ltda for the experiment. After approximately 12 months, 80 seedlings were transplanted into 20 L containers filled with a substrate mixture of sand, coconut powder and goat manure (3:1:1). From these plantlets, samples of young leaves were collected for DNA extraction. Eighty seedlings were classified for resistance to Leptocybe invasa, according to Classification Table 1, proposed in earlier studies, wherein the Fournier index of 1975 was used (Dantas, 2019; Dantas et al., 2021).

Table 1
Classification of Eucalyptus spp. genotypes based on their response to Leptocybe invasa infestation, with: resistance; low susceptibility; moderate susceptibility; and very high susceptibility.

Genotype	Phenotype	Female parent		Male parent	Origin
1404	Resistance		E.urophylla	Eucalyptus spp.	11°35'56”S 38°36'39”W
1249	Resistance		E. grandis	E.urophylla	12°45'47”S 38°20'34”W
1250	Resistance		E. grandis	E. urophylla	12°45'47”S 38°20'34”W
0321	Resistance		E. grandis	E. urophylla	11°56'45”S 38°04'52”W
5341	Resistance		Eucalyptus spp.	Eucalyptus spp.	19°49'16”S 40°16'34”W
1277	low susceptibility		E. grandis	E. camaldulensis	11°35'56”S 38°36'39”W
1262	moderate susceptibility		E. grandis	E. urophylla	12°45'47”S 38°20'34”W
1275	low susceptibility		E. camaldulensis	Eucalyptus spp.	11°35'56”S 38°36'39”W

2.2. Phenotyping

One-year-old Eucalyptus genotype seedlings under L. invasa attack were placed in paper bags and pressed for 48 hours to obtain flat surfaces to take two-dimensional images. For the analysis, a total of 60 leaves of six genotypes were evaluated.

The leaves were arranged in an acrylic tray in groups of 10 and images were captured from both the abaxial and adaxial leaf surfaces. The software captured a total of 453 geometric and morphological characteristics, including leaf area, diameter, elongation, perimeter, circularity, color and texture.

To calibrate the GroundEye® system, the YCbCr color model was employed, with luminance 0 - 1.0, blue values ranging from 0.07 to 0.5 and red values from -0.5 to 0.5. After background color calibration, the images were further analyzed.

Genetic diversity was measured based on Mahalanobis’ Euclidean distance for the variables leaf area, contour deformation, maximum diameter, minimum diameter, irregularity, perimeter, contrast, dissimilarity and homogeneity using R software (R core Team, 2020).

The formula for the Mahalanobis distance is as follows (Equation 1):

$D^2=\left(x-\mu \right)^T*C^\left(-1\right)*\left(x-\mu \right)$ (1)

where: D is the squared Mahalanobis distance; x the data vector of a specific point; μ the mean vector of multivariate distribution; and C^(-1) the inverse of the covariance matrix of the multivariate distribution.

The contribution of the variables to genotype distinction was assessed by principal component analysis (PCA), using R software (R Core Team, 2020). Mean values were used to construct a correlation matrix, from which standardized principal component (PC) scores were derived. The relationships between the variables were examined by correlation analyses.

2.3. In silico study

2.3.1. Data retrieval

Genome sequences from seven eukaryotic organisms were used: E. camaldulensis Dehnh., E. urophylla S.T.Blake, E. tereticornis Sm., E. globulus Labill., E. marginata Sm., E. melliodora A. Cunn. ex-Schauer and E. grandis W. Hill ex Maiden. These sequences, either partially or fully sequenced, were obtained from the GenBank database.

2.3.2. TES sequence and in silico analysis

Transposable elements (TEs) were analyzed in the genomes of Eucalyptus spp. using known TE sequences from genes (CLIRAP1, CLIRAP4, SSR, URP13R, URP17R, URP2R, URP6R). These reference sequences were pairwise aligned and analyzed by BLASTn comparison.

2.3.3. Sequences producing significant alignments

The parameters set for this analysis were as follows: TE copies must not exhibit long segmental changes with a continuous mismatch count of > 10 and, TE copies must have an E-value of < 1E-10. By applying these parameters, a list of TE sequences was obtained comprising individual TE copies from each genome.

2.3.4. Multiple sequence alignment

The TE sequences identified were aligned using Clustal Omega (Sievers and Higgins, 2014). The alignment was visualized and conserved regions were identified with Jalview, a software tool (Waterhouse et al., 2009). The FASTA alignment of the protein sequence was done with MEGA11 software (Tamura et al., 2021). A motif signature was generated using Weblogo (Crooks et al., 2004). The computational structure to analyze the TE sequences is illustrated in Figure 1.

Figure 1
A simplified computational framework based on bioinformatics was employed for the analysis of transposable element (TE) sequences in Eucalyptus.

2.4. Genotyping

2.4.1. Collection of genetic material

Young leaf samples were taken from a total of 80 Eucalyptus seedlings, of which 50 plants were classified as resistant and 30 plants as susceptible to L. invasa. These samples were stored at -20 °C for TE and ISSR marker analysis.

2.4.2. DNA isolation

The DNA from young leaves of each genotype was extracted by a modified Cetyltrimethylammonium bromide (CTAB) extraction method (Doyle and Doyle, 1987). Approximately 200 mg of leaf tissue was ground with 900 µL of 2% CTAB buffer and 50 µL of 0.2% β-mercaptoethanol. The samples were heat-treated at 65 °C for 30 min and then purified by adding chloroform:isoamyl alcohol. After centrifugation, DNA was transferred to a new tube and precipitated using ammonium acetate: alcohol. The DNA pellet was washed, air-dried and resuspended in 70% ethanol. Additional washing with ethanol was performed and the DNA was finally solubilized in a 2-amino-2-hydroxymethyl-1.3-propanediol (Tris) - ethylene diamine tetraacetic acid (EDTA) buffer. The DNA quality and concentration were assessed with a spectrophotometer. The DNA was resuspended at 80 ng/µL and stored at -20 °C.

2.4.3. Genomic DNA quantification

The quality and concentration of DNA samples were assessed using an Epoch® spectrophotometer. Absorbance readings at 260 nm and 280 nm were used for this analysis. To determine the concentration, 1.6 µL of TE buffer was aliquoted into the first well as a blank, while the remaining wells received 1.6 µL of DNA sample from the eight genotypes. The DNA samples were then diluted to a working concentration of 80 ng/µL.

2.4.4. PCR reaction

The target region of the DNA template was amplified using Platus Taq DNA Polymerase. The PCR reactions included 0.3 μL of Taq polymerase, 5 μL of 10X buffer with KCl, 2.5 μL of MgCl₂, 0.5 μL of dNTPs, 2 μL of primers, 4 μL of DNA and 5.7 μL H₂O.

The thermal cycling protocol consisted of initial denaturation at 95 °C for 3 min, followed by 35 denaturation cycles at 95 °C for 30 sec, annealing at 43-64 °C for ISSR markers and 48-67 °C for TEs (Table 2) and an extension step at 72 °C for 1 min. A final extension was performed at 72 °C for 10 min.

Table 2
Transposable elements (TEs), ISSR primer sequences and annealing temperature (Ta).

Transposable elements (TEs)
Primer	Sequence 5' - 3'	Ta °C
*CLIRAP1	CGTACGGAACACGCTACAGA	67
*CLIRAP4	CTTTTGACGAGGCCATGC	50
*SSR	GAGAGAGAGAGAGAGAG AC	60
*URP13R	TACATCGCAAGTGACACACC	48
*URP17R	AATGTGGGCAAGCTGGTGGT	74
* URP2R	CCCAGCAACTGATCGCACAC	65
*URP6R	GCAAGCTGGTGGGAGGTAC	65
ISSR marker
Primer	Sequence 5' - 3'	Ta °C
DAT	GAGAGAGAGAGAGARG	43 °C
UBC 834	AGAGAGAGAGAGAGAGYT	47 °C
Goofy	GTGTGTGTGTGTGTYG	48 °C
UBC809	GTGTGTGTGTGTGTYG	48 °C
Pat 1 – QF	GGTTCCATTGCTTACCGATT	60 °C
Pat 1 – QR	CAAGTTTCGAGGAACAGC	60 °C
Lxx22R	CAAGCAGGGGTACTAACTTCGA	64 °C
Cxx2	ACCCTGTGTTGTTTTCAACG	57 °C
M2	GGGCAGAGAGAGAGAGAGAG	64 °C
UBC810	GAGAGAGAGAGAGAGAT	43 °C
UBC808	AGAGAGAGAGAGAGAGC	47 °C

• ^*
  Long Terminal Repeat (LTR) - CLIRAP1, CLIRAP4 and SSR; Universal Rice Primers (URP).

The presence of amplified fragments was confirmed by agarose gel electrophoresis on 1.5% gel. Electrophoresis was carried out in 1% TBE buffer for 50 min at 100 V, 100 mA and 100 W. Safer dye® was used to stain the fragments, which were visualized under ultraviolet light in a transilluminator (LPIX, Loccus biotechnology).

2.4.5. Data analysis

Private fragments (bands) were quantified and genetic distances were estimated. Weakly stained and poorly defined fragments were excluded. The polymorphism percentage was determined and a binary matrix of presence (1) and absence (0) was constructed. The statistical software GenAlEx (Smouse et al., 2012) was used.

The genetic dissimilarities (dij) between each pair of genotypes were estimated using the Jaccard coefficient, expressed as dij = b + c / a + (b + c).

A simplified representation of genetic distances was achieved using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering method in the DARwin software version 6.0.021 (Perrier and Jacquemoud-Collet, 2019).

3. Results and Discussion

3.1. Phenotyping

The results of the damage analysis revealed a more uniform texture of the resistant genotypes, indicating a smoother and more regular surface, compared to susceptible genotypes. In addition, the gap area of the resistant genotypes was smaller, suggesting a more compact leaf structure. On the other hand, susceptible genotypes had a larger gap area and deformed leaves, indicating a less uniform and more irregular surface (Figure 2).

Figure 2
GroundEye® images of a susceptible genotype to L. invasa (Genotype A) and a resistant genotype (Genotype B): Leaf image (back), Texture representation and geometry representation images.

Leptocybe invasa induces hyperplasia of leaf midribs, petioles and twigs of up to 2-year-old Eucalyptus seedlings and trees, and this deformation results in bump-shaped galls. Gall formation by L. invasa on growing shoot tips and leaves of Eucalyptus accelerates leaf abscission and causes shoot drying. A heavy infestation of the wasp results in loss of vigor and growth, which subsequently results in twig dieback (Kumar et al., 2015).

Gall formation is the main symptom of the insect attack, and can be helpful in identifying the occurrence of the insect on trees, to define effective strategies to reduce the insect population in the plantation (Ortiz et al., 2017).

Figure 3 shows varying degrees of deformation on leaves of plants affected by gall wasps, indicating the presence of galls in different areas of the leaf. This observation confirms similar findings in previous studies. Compared to resistant genotypes, the levels of leaf deformation on susceptible genotypes infested with gall wasps were higher. These results further emphasize the susceptibility of certain genotypes to gall wasp damage and underscore the importance of identifying and exploiting resistant genotypes in breeding programs and pest management strategies.

Figure 3
Genotypes of Eucalyptus spp. with different responses to L. invasa attack. A: Gall-free new leaf of resistant genotype. B: Galls on new leaf of susceptible genotype. C: Galls on branches of susceptible genotype. D: Branch of resistant genotype. E: Branch of susceptible genotype.

Branch deformation was observed, in agreement with Dantas et al. (2021), indicating high L. invasa infestation in the petiole-midrib axis of leaves, petioles and young branches. This infestation can disrupt apical dominance and lead to branching or increased sprouting.

In the context of susceptibility to gall-inducing insects in planted forests, knowledge about morphological responses of hosts is still limited (Sarmento et al., 2021). The presence of L. invasa eggs on plant tissues leads to gall formation, leaf curling and premature leaf aging. Severe infestation can result in shoot death, leaf drop, stunted growth and weaken the tree, causing significant losses in Eucalyptus plantations.

The geometric variables, particularly leaf area and gap area, were found to be the most relevant in explaining the statistical variation. This supports previous studies (Mendel et al., 2004; Tong et al., 2016) that demonstrated the correlation between leaf size and surface damage changes caused by oviposition. These parameters indicate the level of pest activity and plant tissue degradation.

Based on quantitative analyses of damage data, a dendrogram was constructed (Figure 4), which facilitated the identification of two distinct groups: Group I, comprising three resistant genotypes (1404, 1249 and 1250) and Group II, consisting of three susceptible genotypes (1262, 1275 and 1277). Within Group I, genotypes 1404 and 1249 were most similar to each other, while differing from genotype 1250. In contrast, within Group II, genotypes 1277 and 1262 were most similar to each other, but differed from genotype 1275.

Figure 4
Dendrogram of genetic relationships among Eucalyptus genotypes using Mahalanobis distances derived from phenotypic data.

The dendrogram provides insights into the genetic relationships and clustering patterns among the genotypes. Mahalanobis distance analysis demonstrates how damage traits can be used as informative markers for genotype selection. The dendrogram highlights the genetic dissimilarity between the susceptible and resistant genotypes, indicating distinct damage characteristics associated with their response to the pest. The clustering pattern suggested that these genotypes share similar genetic traits related to their susceptibility or resistance.

Based on phenological analysis data obtained from GroundEye®, genotypes 1262, 1275 and 1277 were susceptible and genotypes 1249, 1250 and 1404 resistant. Phenological analysis not only corroborated the data acquired by Dantas (2019), but also enabled a precise differentiation of multiple phenotypes between resistant and susceptible genotypes. This innovation facilitates the identification of genotypes by more objective methods.

Principal component analysis – PCA was employed to identify the most important variables of the data set (Figure 5).

Figure 5
Biplot obtained by the linear combination of variables related to the phenotypic variables of susceptible (S) and resistant (R) Eucalyptus genotypes.

The PCA analysis reduces the number of effective parameters to differentiate genotypes. In addition, leading traits that account for higher fractions of overall variability are summarized and identified and groupings that could not possibly emerge from the raw data are visualized. In this study, PCA, based on a correlation matrix, was conducted to determine the distinction among genotypes. Two principal components, namely “leaf area” and “contour deformation,” were identified, together accounting for 93.6% of the overall variability.

The initial component, denoted as “leaf area,” accounts for 73.3% of the variance, which means that these characteristics have the most significant variability across the genotypes and the greatest influence on their differentiation. The principal loadings on the PC1 axis were associated with irregularity, contrast and dissimilarity, as these factors had the greatest values and were key for genotype distinction. On the other hand, the secondary component “contour deformation” explained 20.3% of the variance. For the second principal component (PC2), the variable with the highest loading was homogeneity.

Leaf area, contour deformation, maximum diameter, minimum diameter and perimeter represented the greatest value on the PC1 and PC2 axis in two components. The remaining components explained less variability. These eigenvalues and their percentages provide insights into the contribution of each axis in explaining the dissimilarity among the factors under study. Higher eigenvalues indicate axes that capture more variation (Nguyen and Holmes, 2019; Stewart et al., 2014).

Figure 6 shows the correlation of variables. Leaf area was positively correlated with contour deformation (r = 0.89), maximum diameter (r = 0.94), minimum diameter (r = 0.94) and perimeter (r = 0.94). Also, leaf area was significantly positively correlated with irregularity (r = 0.49), contrast (r = 0.6) and dissimilarity (r = 0.7). These parameters can therefore be used to predict each other. Meanwhile, a negative correlation was found between homogeneity and all other variables.

Figure 6
Correlations between phenotypic variables of susceptible and resistant Eucalyptus genotypes.

The correlation coefficient can provide information on the most relevant traits to assess genotypes. These parameters can be used to predict others and could be taken into consideration for the characterization of genotypes. It is worth emphasizing that phenotypic analysis is considered a first step in the assessment of genetic diversity of a plant species (Hashemi and Khadivi, 2020).

3.2. In silico study

To analyze sequence conservation, multiple sequence alignment (MSA) was performed by the EMBL-EBI Clustal Omega server. The MSA included seven sequences, with lengths from 75 to 224 nucleotides, and a mean length of approximately 144 nucleotides. By employing appropriate alignment parameters and a substitution matrix, conserved regions, structural characteristics and sequence similarities among the TE copies were identified. The MSA analysis also facilitated the construction of a dendrogram, which represents the phylogenetic relationships among the sequences, providing insights into evolutionary relationships and divergence. The cladogram showed branching patterns, without including branch length information (Figure 7).

Figure 7
Clustal Omega analysis using sequences of transposable elements and the genome sequences of the Eucalyptus spp.

An analysis of the phylogenetic tree revealed the following relationships among the Eucalyptus sequences: a close relationship between E. melliodora and E. marginata, with a branch length of 0.34. These two sequences clustered together with E. globulus, which had a slightly shorter branch length (0.31). Within this cluster, the sequence of E. tereticornis diverged earlier, with a branch length of 0.29. On a separate branch, E. grandis branched off from the main cluster, with a slightly shorter branch length (0.24).

For E. urophylla, the sequences shared a branch length of 0.16. The phylogenetic tree structure indicated a close relationship between E. melliodora, E. marginata and E. globulus, while E. grandis branched off earlier. E. urophylla, E. marginata and E. grandis formed a distinct group (Figure 8).

Figure 8
Neighbor-joining phylogram without distance corrections. Clustal Omega analysis using sequences of transposable elements and the genome sequences of Eucalyptus spp.

Eucalyptus melliodora and E. marginata were closely related (0.23 and 0.24, respectively). These two sequences clustered together with E. globulus, with a branch length of 0.13. Another cluster was formed by these three sequences, with a genetic distance of 0.065. Eucalyptus urophylla diverged from the main cluster (0.17). The Percent Identity Matrix analysis revealed varying degrees of similarity among the sequences. Highest similarity was observed between E. camaldulensis and E. globulus (70.33%) and the lowest between E. grandis and E. camaldulensis (39.66%).

The L. invasa-resistant genotypes used in this study for analysis with TE and ISSR markers were hybrids between E. urophylla and E. grandis. Retrotransposons accounted for the major portion of the E. grandis genome (44.5%), while long terminal repeat retrotransposons were the best represented class (21.9%). The DNA transposons encompassed only 5.6% of the genome. For this class, Helitron elements were the most abundant, with an estimated 15,000 copies or 3.8% of the genome (Myburg et al., 2014).

Multiple sequence alignment generated by Clustal Omega revealed conserved patterns and variations within the TE sequences, indicating the significance of these regions in Eucalyptus genomes. Conservation was high in certain regions, particularly among E. marginata, E. melliodora and E. globulus, suggesting a close phylogenetic relationship. On the other hand, significant variations were observed among E. grandis, E. tereticornis, E. urophylla and E. camaldulensis, indicating higher diversity at these loci.

Using the conservation sequence profiles, a distinct sequence signature was identified for each region, which showed that the areas that encompassed the shared β-hairpin motif were highly conserved. To enhance the visual representation of the marked conservation in these regions, a weblogo was generated (Crooks et al., 2004), which shows the amino acid distribution in colors at each position for each signature motif (Figure 9).

Figure 9
Sequence signatures based on transposons of aromatic (W, F, Y; in green) and aliphatic amino acids (A, V, L, I, M; in black) in Eucalyptus spp.

The analysis of transposon sequences revealed moderate conservation, with varying levels of nucleotide height that indicated their conservation levels. The Weblogo analysis focused on Eucalyptus transposases that consisted of 150 amino acids. Aromatic amino acids, in particular tryptophan (W), phenylalanine (F) and tyrosine (Y), have been identified in Eucalyptus species and are of interest due to their potential role in insect resistance.

Research has shown that elevated levels of these aromatic amino acids contribute to enhanced resistance against a wide range of insect pests. These amino acids are involved in the synthesis of secondary metabolites, such as phenolics and terpenes, known for their insecticidal properties. Studies have explored the use of genetic manipulation techniques to enhance the expression of the phenylalanine ammonia-lyase (PAL) gene, with a view to increase phenolic compound production and improve resistance against specific insect pests. Aromatic amino acids in Eucalyptus are also associated with indirect defense mechanisms against insect pests, as they contribute to the emission of volatile organic compounds that attract natural enemies of herbivores, such as parasitoids or predators, providing an additional layer of defense (Chaudhari et al., 2021).

The relationship between the presence of secondary compounds and the response of Eucalyptus genotypes to L. invasa attacks (Dantas, 2019) was confirmed by the results of in silico analysis.

3.3. Genotyping

The genetic markers DAT, UBC 834, Goofy, UBC809, Pat 1 – QF, Pat 1 – QR, Lxx22R, Cxx2, M2, UBC810, UBC808, CLIRAP1, SSR, URP2R, URP6R, URP16R, URP13R and CLIRAP4 were used to differentiate susceptible from resistant Eucalyptus genotypes to gall wasp infestation. Polymorphic patterns were observed in the amplification profiles generated by these ISSR markers, indicating variations among the analyzed genotypes. These variations suggest the presence of different alleles or genetic variants at the targeted loci. The number of fragments produced ranged from 1 to 5, with a mean of 2 fragments per oligonucleotide, resulting in a total of 36 fragments.

Data analysis with GenAlEx provided insights into the percentage of polymorphic loci within the studied groups. A lower percentage of polymorphism (30.56% of variation) for susceptible plants was detected. On the other hand, resistant plants had a higher polymorphism level (77.78%). The mean percentage of polymorphic groups was estimated at 54.17%, with a standard error (SE) of 23.61% (Figure 10).

Figure 10
Band patterns across Eucalyptus genotype groups in response to L. invasa based on cumulative data derived from TE and ISSR analysis: Susceptible and Resistant.

Genetic data analysis identified distinct band patterns for the susceptible and resistant genotypes represented in the study, i.e., a total of 17 and 33 bands, respectively, for the susceptible and resistant genotypes.

The presence of private bands, unique to each group, was also examined. Susceptible genotypes had three private bands, indicating specific genetic markers exclusive to this Group. In contrast, resistant genotypes had a higher number of private bands (a total of 19).

These private bands contribute to the genetic distinctiveness and uniqueness of each group. The presence of private bands indicates specific genetic differences for DNA regions present in some genotypes but absent in others. Previous studies described the relationship between genetic diversity and resistance to stress or pathogens (Abrinbana et al., 2010; Suzuki et al., 2022).

The analysis further considered the degree of shared bands, known as LComm bands, within and between the group. In this case, neither susceptible nor resistant genotypes had any bands within the categories LComm bands <=25% or <=50%, suggesting few shared genetic markers.

To assess the genetic diversity within each Group, measures such as Mean Expected Heterozygosity (He) and Mean Unbiased Expected Heterozygosity (uHe) were calculated. Susceptible genotypes had a Mean He of 0.102, indicating moderate levels of genetic diversity. The Standard Error (SE) of Mean He of the Groups was 0.027, reflecting the precision of this estimation. Similarly, the Mean uHe calculated for susceptible genotypes was 0.122, with an SE of Mean uHe of 0.032. In contrast, higher levels of genetic diversity were found for the resistant genotypes, with a Mean He of 0.285 and an SE of Mean He of 0.032. The Group Mean uHe was calculated as 0.317, with an SE of Mean uHe of 0.036. These results suggest higher genetic diversity of resistant than susceptible genotypes.

Free Tree software was used to construct a genetic similarity matrix, based on eight samples (1277 S, 1262 S, 1275 S, 1249 R, 1250 R, 1404 R, 0321 R and 5341 R). The analysis detected a range of similarity values among the analyzed samples. The highest similarity (58%) was detected between samples 1277 S and 1275 S. Samples 1275 S and 1277 S were also highly similar to sample 1250 R (50 and 58%, respectively). On the other hand, similarities were lowest (25%) between samples 1249 R and 5341 R. Low similarity was also found between sample 0321 R and samples 1277 S, 1275 S and 1250 R (28, 44 and 26%, respectively).

The dissimilarity data obtained from these primer sets were analyzed to construct trees using TEs and ISSR methods. Transposable Elements and ISSR primers are commonly used in dissimilarity studies due to their ability to detect polymorphisms in different genomic regions (Amiteye, 2021). The dissimilarity values ranged from 20 to 65%. The equality threshold was set to 0%, to ensure that only identical units were grouped together (Figure 11).

Figure 11
Dendrogram resulting from UPGMA cluster analysis using Jaccard's dissimilarity coefficient for (A) TE-based markers and (B) ISSR-based markers. Red represents genotypes classified as susceptible, while green represents resistant genotypes.

The dissimilarity data used for tree construction were obtained from the analysis of TE primers (Figure 11A). The dissimilarity values ranged from 12 to 75%, reflecting the genetic differences among the analyzed units. The tree construction process involved several iterations. The initial iterations grouped genotypes 5341 and 1249 (resistant) together, followed by the grouping of genotypes 1404 and 0321. Subsequent iterations led to the formation of additional groups, ultimately resulting in the last cluster, represented by genotype 1262, 1277, 1250 and 1275.

The edges of the final tree represented the dissimilarity between the connected units. The sum of the edge length was calculated as 1.0, indicating the overall genetic dissimilarity among the units in the tree. The dissimilarity data used for tree construction were obtained from the analysis of ISSR primers (Figure 11B). In the first iteration, node 9 was formed by grouping (susceptible) genotypes 1277 and 1275. In the second iteration, nodes 10 and 11 were formed by genotypes 5341 and 1249 and units 1262 and 1250, respectively. Subsequent iterations led to the formation of additional nodes, ultimately resulting in the last group represented by node 15, which consists of nodes 14 and 13. The edges and their respective lengths in the final tree were calculated. The edge lengths ranged from 0.042 to 0.14, with a total sum of 1.339.

In this way, the resistance levels among the genotypes 5341, 1249, 1404, 0321, 1277 and 1275 could be differentiated, consistent with the phenotypic analysis.

Molecular markers such as ISSR (Inter Simple Sequence Repeats) provide valuable information on interrelationships among plant genotypes (Azizi et al., 2019; Odesola et al., 2021; Ojuederie et al., 2020).

In view of the potential impact of transposable elements (TEs) on gene regulation, it is important to note that TEs can have diverse effects on neighboring genes, pre-RNA processing and coding sequences (Lee and Rio, 2015). This can influence plant response to stressful situations as that of insect pest attack (Graveley, 2005).

4. Conclusions

It was possible to distinguish gall-wasp-susceptible from - resistant genotypes by image analysis.

In silico analysis identified conserved regions in the Eucalyptus spp. genome, associated with proteins involved in secondary metabolite production, such as terpenes, which play a role in the plant response to Leptocybe invasa.

The discriminatory capacity of TEs and ISSR primers was confirmed and bands were generated and used to identify resistant genotypes. However, in both cases, it is suggested to increase the minimal number of markers used to discriminate genotypes.

Acknowledgements

We thank the Coordination for the Improvement of Higher Education Personnel—Brazil (CAPES-001).

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Publication Dates

• Publication in this collection
  31 May 2024
• Date of issue
  2024

History

• Received
  29 Oct 2023
• Accepted
  05 Apr 2024