Identification of cry genes in Bacillus thuringiensis by multiplex real-time PCR

doi:10.21203/rs.3.rs-1643201/v1

Bacillus thuringiensis is an important bacterium of the group Bacillus cereus sensu lato due to its insecticidal properties. This microorganism has high genetic variability and its strains produce different Cry toxins, known as δ-endotoxins, which are mainly responsible for its toxic effect on insects that are agricultural pests or vector human diseases. Each strain can express a variety of cry genes, out of a total of 789 cry genes described so far. The detection of these genes is very important to characterize strains, as they may indicate their toxic potential. Several methods have been used to characterize B. thuringiensis strains, but one of the most common techniques is Polymerase Chain Reaction (PCR) from primers that detect the presence of cry genes. This technique has been optimized to make real-time multiplex quantitative PCR (qPCR) assays faster, more efficient, and safer, because the presence of three genes can be detected in a single reaction. In this work, a multiplex assay was developed to identify the presence of genes from the cry1A, cry1C, and cry1F families whose respective toxins are present in both bioinsecticides and commercial transgenic plants used to control caterpillars. Specific primers were designed to identify the families of the cited genes and the system was validated with samples that were sequenced by next-generation sequencing (NGS). The system was implemented and used to characterize 214 strains. Of these, eight were submitted to conventional PCR, and the results matched, again validating the system. Thus, the application of the proposed technique allows the reliable evaluation through this system to detect the presence of the genes of the families cry1A, cry1C, and cry1F in samples of B. thuringiensis.

gene diversity

singleplex qPCR

triplex qPCR

AMOVA

Bacillus thuringiensis (Bt) is a Gram-positive bacterium with entomopathogenic activity and several advantages over other bacteria. This ubiquitous species, which is innocuous to humans and other vertebrates, is found in a variety of environments such as soil, dead insects, leaf surface, and inside plant tissues (Monnerat et al. 2009).

The life cycle of this bacterium has two significant phases that produce proteins with insecticidal activity: the vegetative phase (where the Vip and Sip toxin genes are expressed), in which cell division occurs, and the stationary phase, where sporulation occurs (Bravo; Gil; Soberon, 2007; Ibrahim et al., 2010) In the latter, crystalline parasporal inclusions, composed mostly of δ-endotoxins, are released and liberated in cell lysis (Habib and Andrade, 1998). These toxins, also known as Cry and Cyt, have been used to control insect that are agricultural pests and disease vectors (Palma et al., 2014).

In general, the Cry, Cyt, Vip, and Sip families of proteins exert activity on different orders of organisms such as Lepidoptera, Diptera, Coleoptera, Rhabditida, Hemiptera, Hymenoptera, and Gastropoda. The three-domain (3-D) Cry toxins, because they comprise a large part of the proteins responsible for toxicity to several orders of insects, are better elucidated and studied than the other toxins produced by B. thuringiensis (Palma; Berry, 2016). The efficacy and specificity of B. thuringiensis strains in insect control favor the formulation of biopesticides based on this bacterium. Throughout the world, more than 100 formulations have been marketed, since the first product was launched and are responsible for more than 90% of the bioinsecticide sales (Pardo-López et al., 2013). With the advent of genetic engineering, cry genes were inserted and recoded to express the protein in plants, reaching target insects, such as young Ostrinia nubilalis caterpillars that enter the apical bud and physically damage the plant, and consequently cause financial losses (Pardo-lópez, Soberón, Bravo, 2013, Rubio-infante, Moreno-Fierros, 2015). Some proteins of the Cry1A family, for example, are expressed in corn and Bt cotton plants and have insecticidal activity for lepidoptera and beetles that are pests of these crops. Of the 36 corn varieties approved in Brazil, 19 contain one or more genes from the cry1A or cry1F families (Qiu et al., 2015).

Most commercial plants express the same genes of B. thuringiensis that are present in the bioinsecticides, even if in these are in the pro-toxin form. The major bioinsecticides marketed worldwide have the Cry1Aa, Cry1Ab, Cry1Ac, Cry1B, Cry1C, Cry1D proteins. The presence of the proteins in transformed products and plants demonstrates that the main proteins used in pest control are those of the families Cry1A, Cry1C, and Cry1F (Baktavachalam et al., 2015).

A threat to the use of Bt-plant technology and formulations containing B. thuringiensis in their composition is the selection of insects resistant to Cry toxins. Resistance has already been documented at different levels for some insect species such as Plodia interpunctella, Cadra cautella, Leptinotarsa decemlineata, Chrysomela scripta, Spodoptera littoralis, Spodoptera exigua, Spodoptera frugiperda, Heliothis virescens, Ostrinia nubilalis, and Culex quinquefasciatus (Tabashnik; Rensburg; Carrière, 2009). The toxins with reported resistance are Cry1A, Cry1F, Cry2A, Cry3Bb, and Cry1C (Monnerat et al., 2015; Carrière; Fabrick; Tabashnik, 2016).

The mechanisms of insect resistance to B. thuringiensis proteins can occur due to more than one change in the mode of action steps of the toxins. This may manifest as changes in the activation of the toxin or the inability of the toxin to bind to receptors in the gut of the target insect. The problem is even more serious in cases of cross-resistance where different toxins have the same binder. An example is resistance to the Cry1C protein in Plutella xylostella populations resistant to Cry1A (Pardo-lópez; Soberón; Bravo, 2013). A more recently reported case occurred in populations of S. frugiperda resistant to toxin Cry1F, and also present cross-resistance to the toxins Cry1Aa, Cry1Ab, and Cry1Ac (Monnerat et al., 2015). To overcome potential resistance, some management alternatives are proposed, such as the combination of high doses of Cry toxin with the use of refuges (Monnerat et al., 2015). Allied to the refuge, another alternative to monitor and find ways to control resistant insects in the field is to screen and isolate new cry genes from nature or in vitro genetic improvement of gene families to amplify toxicity against pests or recovery toxicity if they already face resistance (Bravo et al., 2013). The product, Agree®, which has proteins from the Cry1A and Cry1B-like families efficiently controls and manages the resistance of populations resistant to Cry1F (Fernandes et al., 2015).

Therefore, knowing the gene content of strains that may be used in formulation of biological products is essential for management actions, to avoid using strains expressing the same genes that are exerting field selection pressure, which could increase the speed of selection by potential resistant insects. Thus, this work aimed to develop a multiplex system to identify cry gene families by qPCR based on hydrolysis probes to identify genes of the cry1A, cry1C, and cry1F families, whose respective toxins are present in both bioinsecticides and transgenic plants used to control lepidopteran-pest.

1- Strains selected

The strains of B. thuringiensis used in this work belong to the collection of invertebrate bacteria from Embrapa Genetic Resources and Biotechnology (Monnerat et al., 2002). This collection contains 2400 Bt strains, and for the design of this study, 10% of the total strains stored were randomly selected.

2- Genes of interest selected

The genes selected for the TaqMan® assays were cry1A, cry1C, and cry1F. The DNA sequences of the genes that were used to design the molecular strategies were made available on GenBank and were chosen from the existing links at the Bacillus Bank, which is maintained by Dr Neil Crickmore and is available at https://www.bpprc.org/.

To generate the consensus sequence of each gene, 109 sequences were used for the cry1A, 17 for cry1C, and 10 for cry1F.

3- Primers and probes

First, the corresponding consensus sequences of each of the genes (cry1A, cry1C, and cry1F) were generated using the program Geneious® v9.1.5 (Kearse et., 2012). TaqMan® primers and probes for each gene were designed in The Primer Express Software v3.0 (Applied Biosystems). The specificity of primers and probes was determined by the Basic Local Alignment Search Tool (BLAST) algorithm in GenBank (Benson et al., 2013). For each of the designed probes, a fluorophore and an attenuator were chosen to fit the four filters of the thermocycler StepOnePlus™ System. TaqMan® probes were conjugated to the specific VIC, FAM, and NED fluorophores. The quencher was determined using the MGB and QSY® molecules. The hydrolysis probes for the cry1A, cry1C, and cry1F genes were synthesized by Thermo™ Custom TaqMan® Probes at a concentration of 20X.

4- Real-time PCR reaction

4.1. The efficiency curve determined

For the multiplex assays, 10 µl of TaqMan™ Fast Advanced Master Mix (Promega), 0.4 µl for each 20X assay, 2 µl of 10 ng/µl DNA, and milliQ water were added to reach 20 µl. The amplification cycle was defined for the rapid reaction mode so that the activation of the polymerase occurred for 20 s at 95°C, denaturation occurred for 1 s at 95°C and extension/annealing for 20 s at 60°C. DNA used to detect the three cry genes and standardization of the assay was obtained from the Bt strain designated A80 (GenBank accession number SAMN12229590), which was previously sequenced by NGS technology and contained at least one copy of each of the genes.

Real-time PCR assays for each sample (cry1Aa, cry1C, and cry1F) were performed in triplicate, and all assay optimization procedures followed the standard established in MIQE (Bustin et al., 2009).

The efficiency curve of the assays was determined by linear regression of the dilution points. DNA from A80 strain was diluted 1:10 at five points, with an initial concentration of 100 ng/µl. Initially the dilution was performed for each test to determine the curve of the singleplex reaction. After the optimized reaction for each probe, the curve for the three assays was done in the same multiplex reaction. From the obtained values, the regression plot was established to define the amplification efficiency of the reaction (Bustin et al., 2009). The lower limit of detection (LOD) of the minimum sample quantity in which the result will be within the reliability was defined using the formula established by Shrivastava and Gupta (2011). All real-time PCR assays were completed in triplicate.

4.2 The methodology developed to detect genes by multiplex qPCR validated

For DNA extraction, the Bt strains were cultured in EMBRAPA-agar medium for 16 h (Monnerat et al., 2007). The colonies were collected using a sterile disposable loop with a 10 µL loop and added to 200 µL of sterile deionized water in a 1.5 mL polypropylene tube, and then homogenized. Next, 1 µl of lysozyme (25 mg/mL) was added, and the sample was homogenized and incubated for 2 h at 37°C. The DNA was extracted using the Maxwell® 16 Cell DNA Purification Kit on the Maxwell® 16 Instrument (PROMEGA) according to the manufacturer’s instructions. The extracted DNA was quantified on a Quantus™ Fluorometer (PROMEGA) with the QuantiFluor® ONE dsDNA System (PROMEGA) kit according to the manufacturer’s instructions. The purity of the DNA in the samples was determined by analyzing the absorbances at wavelengths of 260 nm and 280 nm in the Picodrop ul Spectrophotometer (Picodrop).

To validate the multiplex assay, we chose ten strains of Bt, whose gene content was determined by NGS sequencing (data not shown) and were used as standard to validate the assays.

4.3 Strains characterized by qPCR

Two hundred and fourteen strains of B. thuringiensis were selected at random. They originated from different localities and are toxic to insects. These strains were then subjected to qPCR characterization using the previously developed assay to determine the frequency of the cry1A, cry1C, and cry1F genes.

The results of the presence and absence of the genes were submitted to the statistical analysis using the programs PAST: Paleontological Statistics Software (Hammer et al., 2001) and Arlequin suite version 3.5 (Excoffier; Lischer, 2010).

Primers and probes

Amplification assemblages (Table 1) obtained from the consensus sequences of the three cry1A, cry1C, and cry1F genes were found to be specific using in silico analyzes. BLAST analysis, which was performed to identify other sequences that could be a non-specific target for the assays, demonstrated that the identity and alignment of the component sequences of each assay with the sequences of their target genes was 100% and the values of e-value were all close to zero, indicating that the assays are specific to their targets (Amaral; Reis; Silva, 2007).

Table 1

Characteristics of the TaqMan® primers and probes used to detect three *cry* genes of *B. thuringiensis*.
Gene	Sequence (5`→ 3`)	Start position	Final position	Size (pb)	Tm (°C)	GC%
Cry1A	CCA ATC GAT ATT TCC TTG TCG C (forward)	250	271	22	59.4	45
	CAT TGA GAG GGA CCA AAA ATT CC (reverse)	343	365	23	59.7	43
	NED GAA TTT GTT CCC GGT GCT MGB (probe)	291	309	19	70	47
Cry1C	CAG GAC CAG AGT AAT TGA TCG CTT (forward)	670	693	24	59.6	46
	AGA TGC AGA TTG GCC GCT T (reverse)	780	798	19	59.4	53
	VIC CTA CTT GAA AGG GAC ATT C QSY (probe)	710	728	19	69	42
Cry1F	GGA AGA TGT GCG TAT TCG ATT TG (forward)	25	47	23	59.5	43
	CCG CTT GAA CAT AGA CCG ATA AA (reverse)	115	137	23	59.1	43
	FAM ACG ACG CTT TAA TAA CAG CA MGB (Sonda)	57	76	20	70	40

Assays optimized

The standard Bt sample used to optimize the DNA extraction and amplification conditions in the qPCR assays for cry gene detection is identified as A80, which was used because it had already been sequenced by NGS. Mounting and annotation of this strain helped identify a copy of the cry1A, cry1C, and cry1F genes in the genome of that strain.

The qPCR assays were optimized for 20 µL of reaction, setting the optimum test concentration to 0.022X. The ideal DNA concentration to be used in the assays was determined in 20 ng/µL. The annealing temperature was defined as 60°C (Fig. 1).

Once the amplification conditions were established, fluorescence emission was detected from each of the fluorophores that were added to the TaqMan® probes designed to detect the cry genes. The wavelength of 520 nm was detected for the cry1F gene (FAM), 550 nm for the cry1C gene (VIC), and 580 nm for the cry1A gene (NED).

Efficiency curve determined

The efficiency curve of the multiplex assay was determined by serial dilution of A80 sample (Table 2).

Table 2

Efficiency curve of the assays to detect single *cry* genes (singleplex) and together (multiplex).
	Target	Slope	Y-Intercept	R²	Eff%	Error
Singleplex	cry1A	-3.299	22.009	0.995	100.987	0.062
	cry1C	-3.287	24.773	0.996	101.466	0.063
	cry1F	-3.274	20.894	1.000	102.027	0.016
Multiplex	cry1A	-3.259	21.569	1.000	102.686	0.014
	cry1C	-3.300	26.569	0.997	100.940	0.032
	cry1F	-3.255	21.478	0.999	102.875	0.015

The efficiency obtained in both the singleplex and multiplex assays was within the expected range according to the MIQE protocol (Bustin et al., 2009), which is between 90% and 110%. These results indicate that competition between the primers and probes in the same reaction did not affect the multiplex assay (Taylor et al., 2009). The slope values are within the expected range from − 3.58 to -3.10; for a curve with 100% efficiency, the expected slope value is -3.32 (Bustin et al., 2009).

To determine the linearity of the curve, the R² values were higher than the established values of 0.98 (Bustin et al., 2009; Broeders et al., 2014), which indicates that the errors in pipetting were minimal, and there is a linear correlation between Cqs and variations in DNA concentration. The comparative analysis between the Cqs variation as a function of the dilutions in both the singleplex and multiplex assays are presented in Table 3.

Table 3

Mean Cqs variation of the efficiency curves of the singleplex and multiplex assays as a function of DNA dilutions.
DNA concentration (ng/uL)			Cqs Variation (∆Cq)
	cry1A		cry1C		cry1F
	singleplex	multiplex	singleplex	multiplex	singleplex	multiplex
100	14.24713	14.78821	15.22248	15.54553	14.96518	15.35871
10	17.47671	17.91811	18.87335	18.79439	18.14298	18.52899
1	20.60076	21.0259	22.4201	21.81687	21.43767	21.75162
0.1	23.78036	24.40196	25.97912	25.19962	24.76557	25.03744
0.01	27.27221	27.81821	28.98219	28.63301	28.0485	28.4992

The results obtained in all the tests found that the variation between the Cqs in the singleplex and multiplex conditions were not greater than one, independent of the concentration of DNA used. Therefore, the probes in the multiplex assay did not significantly compete.

The efficiency curves varied 3 Cqs as a function of the serial dilution of the DNA (Fig. 2). This result is in accordance with the guidelines established in the MIQE (Bustin et al., 2009).

The threshold set for each of the assays was 0.040 for cry1A and cry1C; 0.140 for cry1F. Subsequently, the limit of detection (LOD) for the genes in the multiplex assay was determined. This result demonstrates the minimum concentration limit of the sample in the reaction for the detected genes. For cry1A, cry1C, and cry1F the LOD values were 0.012 ng/µL, 0.065 ng/µL, and 0.014 ng/µL, respectively.

Assay validated comparing with sequenced samples

Comparative analysis of the two techniques cited (NGS and qPCR) found that the results obtained by the amplification using the qPCR technique were the same as the NGS results (Table 4). The multiplex assay detected the presence of the cry1C gene in the A60 sample. This gene had not been previously annotated in the genome analyzes of this Bt strain. To confirm this result, a BLAST analysis was performed and confirmed the gene sequence, indicating that this technique is very sensitive to the presence of the genes studied here.

Table 4

qPCR characterization of the NGS samples to detect the *cry* genes in the ten previously selected strains of Bt.
Sample	Genbank	cry1A		cry1F		cry1C
Sample	Genbank	qPCR	NGS	qPCR	NGS	qPCR	NGS
N110	SAMN12046285	-	-	-	-	-	-
N113	SAMN12169346	-	-	-	-	-	-
N114	SAMN12200924	-	-	-	-	-	-
N115	SAMN12206555	-	-	-	-	-	-
N128	SAMN12211138	-	-	-	-	-	-
A60	SAMN12285942	+	+	+	+	+	+
A68	SAMN12286296	+	+	-	-	-	-
A72	SAMN12289228	+	+	-	-	-	-
A78	SAMN12229590	+	+	+	+	+	+
A79	SAMN12230145	+	+	-	-	-	-
Caption: (+) indicate amplification and (-) absence of amplification.

Of the 214 Bt samples analyzed, 80 samples (37.38%) amplified for at least one cry gene (samples classified A1 to A80) (complementary data) and 134 samples (62.62%) did not present signal amplification (samples classified from N1 to N134) in the developed assays.

Bt cry genes identified by qPCR

Analysis of the tested Bt strains found a significant frequency distribution of the three cry genes (Fig. 3).

From the gene families analyzed by the qPCR assays, the cry1A family genes were the most detected in the multiplex assays (55.37%), followed by the cry1F (30.58%) and cry1C (14.05%). Analysis of the Bt gene diversity indicated that the Shannon index (H’) was 0.96. For the Simpson index (D), the diversity value was 0.58. These values indicate diverse distribution of cry1A family genes in Bt. Analysis of the combination of genes observed that cry1A/cry1F was the most common combination with 39.34%, followed by cry1A/cry1C (27.89%), and cry1C/cry1F (16.39%). The presence of the three genes was identified in only 16.39% of the samples (Fig. 4).

The diversity of the cry gene combination indicated a Shannon diversity index (H') of 1.32. Simpson’s diversity index (D) indicated a diversity value of 0.93, suggesting a greater distribution of these three genes in the Bt strains. None of the combinations were constant in the real-time PCR detections.

The Analyzes of Molecular Variance (AMOVA) found high variability in the distribution of cry genes in all samples (Table 5).

Table 5

Analysis of Molecular Variance (AMOVA) of three *cry* genes by qPCR sampled from Brazil.
cry genes sampled from the Bt strains isolated only in Brazil
Source of variation	Sum of squares	Variance components	Percentage variation
Among populations	8.943	0.00559	0.96374
Within populations	29.296	0.57444	99.03626
Total	38.239	0.58003

The AMOVA indicated that the largest source of variation occurs within the samples (FST = 0.00964 and P-value = 0.41465), confirming the diversity results. The same result was observed when the AMOVA was performed considering only the samples collected in Brazil (Table 6).

Table 6

Analysis of Molecular Variance (AMOVA) of three *cry* genes by qPCR sampled from Brazil and two other countries.
cry genes sampled from Bt strains isolated from Argentina, Mexico and Brazil
Source of variation	Sum of squares	Variance components	Percentage variation
Among populations	9.954	0.00722	1.28080
Within populations	32.296	0.55683	98.71920
Total	42.250	0.56406

AMOVA also indicated that the greatest source of variation occurs within Brazilian samples (FST = 0.00964 and P-value = 0.41465), reinforcing the diversity analyzes.

The real-time PCR technique has been applied to identify several species of Bacillus, such as B. anthracis (Wielinga et al., 2011)d cereus (Cattani et al., 2016). One possibility for basic differentiation of B. thuringiensis from other Bacillus is by identifying cry genes, because these are specific to B. thuringiensis.

The literature contains some reports that detect cry genes using the real-time PCR technique with hydrolysis probes, but none in triplex assays. The analyzed studies have detected cry genes under different experimental conditions. For example, Guidi et al. (2010) used hydrolysis probes to directly detect Bt in soil samples using the cry4Aa and cry4Ba genes as markers. Crighton et al. (2012) used a probe to detect the cry1 gene in studies about dispersion of Bt spores in a closed environment. Cry genes have also been used to detect transgenic events, such as the cry2Ae gene in cotton (Li et al., 2014) and cry1Ac in Oryza sativa (Sajjad et al., 2017).

In this work, the qPCR technique was used to identify multiple cry genes in a single reaction, reducing the time and cost of analyzes. The probes and primers were designed for the conserved regions of the N-terminal domain encoding part of the N endotoxin (pfam03945) of the selected cry genes. In the previously mentioned works, probes were designed primarily for the C-terminal regions of these genes. The in silico analysis demonstrated that the strategy adopted was able to detect the conserved regions of the genes under study with high specificity, as the values of identity (100%) and e-value (0.0) were within the expected range.

Both singleplex and triplex probe sets were sensitive in detecting cry genes (an important condition for this analysis). In the available literature, detection of cry genes by real-time PCR is linked to the quantity of spores per gram of soil, number of gene copies per square meter, or number of copies of the gene per microliter. Thus, the three cry genes could be detected simultaneously with LOD values in the range of 0.012 ng/µL to 0.065 ng/µl of total DNA. These results were obtained with efficiency tests that ranged from 100.987–102.027% in the singleplex. While in the triplex tests, the efficiency ranges from 100.940–102.875%, demonstrating the possibility of using TaqMan probes® to detection simultaneously three cry genes with high specificity and sensitivity from reduced amounts of DNA samples. In other analyzed studies (Li et al., 2014), the efficiency values ranged from 90–95% for probes in singleplex assays.

From the conditions established for the triplex, studies were carried out to verify the abundance, distribution, and diversity of the cry genes among the B. thuringiensis isolates. The search for cry genes is mostly based on the conventional PCR technique, which can detect the presence of new cry genes and direct bioassay work (Ceron et al., 1995; Bravo et al., 1998; Ibarra et al., 2003; Porcar; Juárez-Perez, 2003).

However, the use of real-time PCR to identify B. thuringiensis plus detect and analyze the distribution and diversity of cry genes is still poorly applied. In the assay, all cry1A (Cry1Aa1 to Cry1Aj1), cry1C (Cry1Ca1 to Cry1Cb3) and cry1F (Cry1Fa1 to Cry1Fb7) families were used in the cry gene bank to obtain the consensus sequence and design sets of primers and probes for the gene region that encodes the N-terminal portion of the Cry protein. This strategy, when applied to the Bt samples, identified cry1A family genes as the most abundant (55.37%) followed by cry1F (30.58%) and cry1C (14.05%) genes. These findings were similar to those found in the literature regarding the abundance of the cry1A gene, commonly reported in several articles. However, interestingly, the cry1F gene was more abundant than the cry1C gene, because the literature consulted indicated the opposite in terms of the abundance of these two genes. These results in the literature may be due to the use of PCR primers for the cry1Fa2 and cry1Fb genes only.

Several studies have screened for cry genes using conventional PCR primer oligonucleotides. For example, Porcar; Juárez-Perez (2003) used PCR primers to detect the genes of the cry1 family (cry1Aa, cry1Ab, cry1Ac, cry1Ad, and cry1Ae), cry1C (highly variable region) and cry1F (highly variable region). These researchers found that the cry genes most common in nature are from the cry1 family. Genes from the cry1A family were present in more than half of the analyzed strains (results were obtained from studies that analyzed from 58 to 500 strains of Bt). The cry1C genes also have well-reported abundance while the cry1F genes occur less. The findings of this work are corroborated with the H' = 0.96 and D = 0.58 indices found, indicating important diversity in the cry gene distribution.

Shishir et al. (2014) used PCR primers to detect several families of cry gene (cry1 to cry11) and observed that the cry1 genes were the most abundant (30.8%). Salama et al. (2015) also used PCR primers for diversity studies and found that cry1 family of genes were the most abundant (83.33%). In these two works, the universal primers used detected the genes of the families cry1Aa5, cry1Ac5, cry1Ad, cry1Ae, cry1Ba, cry1Bb, cry1Ca1, cry1Da, cry1Da, cry1Ea3, cry1Eb, cry1Fa2, cry1Fb, cry1G, cry1H, cry1Hb, cry1Ja, and cry1K, for example.

Our strategy for detecting cry genes by real-time PCR proved to be a robust strategy to detect several strains of Bt with several potential genes to be screened and studied.

The evaluation for the presence of the combination of cry genes in the analyzed Bt strain found that the cry1A/cry1F combination was the most common (39.34%), followed by cry1A/cry1C (27.89%) and cry1C/cry1F (16.39%). The presence of the three genes occurred in only 16.39% of the samples. These findings were corroborated by the Shannon (H') diversity indexes of 1.32 and Simpson (D) of 0.93, which suggests that these three genes are broadly distributed in the Bt strains.

These findings are corroborated by literature data. Porcar and Juarez-Perez (2003) reported a high frequency in the combination of certain cry genes such as cry1C-cry1D. The cry1C can be encountered independent of the cry1D (Kim et al., 1998). The cry1D gene has been frequently found alone, but the cry1C gene is always associated with cry1D. Cry1C-cry1D are located in the same replicon where the cry1C gene is downstream of an IS (Insertion Sequence). This may be responsible for the mobility of that gene in Bt strains. From this information and the qPCR results obtained in this study, the NGS results can be associated with two Bt strains that were already sequenced (data not shown). The results allowed identification of changes in the cry1C gene of the two strains. These two genes had alterations in the part of the gene that encodes the C-terminal portion of the Cry1C protein. One such change was the insertion of an IS of 150 bp and the other resulted from duplication of a portion of the cry1Ac gene in the final region of the cry1C gene. In addition, the cry1D gene in both strains could be identified. This gene was located upstream of the cry1C gene, as in the literature data.

Fiedoruk et al. (2017) described the origin of cry1 genes. These genes occur both individually and as part of an insect pathogenicity island (PAI). Cry1 genes occur as gene cassettes located in megaplasmids. Thus, the identification of a cry1 gene by the probe developed in this work may serve as a basis for exploration activities of other cry family genes.

Although many studies based on PCR are reported for cry gene screening, several factors limit their comparison, such as the use of different primers, variation in PCR conditions, and the number of strains and genes analyzed. Another important limitation is the lack of a precise preliminary characterization of the isolates. Some collections do not follow this preliminary step, and this may strongly influence the apparent genetic diversity of the collection (Porcar, Juárez-Perez, 2003).

Therefore, due to the results provided by the assays developed in this work, the use of Taqman probes could be an example of an approach to standard the assays to identify cry genes and their diversity and distribution. This diversity in cry gene content in a B. thuringiensis collection is influenced by the preselection process of the samples to be analyzed in a study and this procedure may only partially reflect the actual genetic diversity of the naturally occurring strains in a location. Another factor to be taken into consideration is the environmental diversity of the geographic area analyzed, which may also contribute to the high number of cry gene patterns in relation to the number of samples analyzed. Thus, due to the few studies that combined all these aspects, further studies are needed to obtain a more concise conclusion about cry gene diversity. An ideal study should combine the analysis of many analyzed samples with a high diversity of natural environments (Porcar, Juárez-Perez, 2003).

In addition, there is a need to establish an index that can better define diversity, not only by the positive reaction in a PCR reaction but following the analysis of some parameter of environmental analysis. In this work, we analyzed 240 Bt strains that resulted in 80 strains with positive amplification for one of the sampled cry genes. The design of the assays for the region of the cry gene to be detected can be a key factor in the amplification and definition of the identified gene score in a collection of sampled strains. This event is particularly impacted by the natural diversity of a particular geographic region, such as Brazil, which contains several vegetation regions that correspond to diverse biomes, from hot humid forest environments, through savanna-like vegetation, and temperate vegetation formations. The strategies of prospecting for the cry genes sought to contemplate all these biomes to cover the greatest possible diversity, observing a great diversity and variability in the distribution of these three cry genes from the performed AMOVA analyzes.

This diversity can also be influenced by the relationships of the Bt strains with the entomofauna in the environment, and populations may be present with naturally resistant or sensitivity to a certain Cry toxin (Monnerat et al., 2015).

COMPLIANCE WITH ETHICAL STANDARDS

Funding: This study was funded by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico); Embrapa Recursos Genéticos e Biotecnologia.

CONFLICT OF INTEREST: Paulo Roberto Queiroz declares that he has no conflict of interest. Marina Cassago Posso declares that she has no conflict of interest. Érica Soares Martins declares that she has no conflict of interest. Priscila Grynberg declares that she has no conflict of interest. Roberto Togawa declares that he has no conflict of interest. Rose Gomes declares that she has no conflict of interest.

ETHICAL APPROVAL: This article does not contain any studies with human participants or animals performed by any of the authors.

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Identification of cry genes in Bacillus thuringiensis by multiplex real-time PCR

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