Abstract
Background
This study investigates the gene expression dynamics and biocontrol
effectiveness of Beauveria bassiana against Spodoptera frugiperda, the
fall armyworm, a notable agricultural pest. Our objectives were to
analyze the B. bassiana gene expression variation during insect
infection compared to grow on artificial media and to evaluate the
effects of different spore concentrations on larval mortality,
development, and reproduction.
Methods
A combination of bioassays and transcriptome analysis was employed.
S. frugiperda larvae were exposed to different spore concentrations,
and mortality rates were recorded at various developmental stages. RNA
sequencing was performed on fungal samples from infected larvae and
those grown on 1/4-strength Sabouraud Dextrose Agar with Yeast Extract
(SDAY) media. Differential gene expression libraries were constructed
at 48, 96, and 144 hours’ post-infection. Gene Ontology (GO) and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to
identification of biological processes and pathways that differentiate
infection from growth on artificial media.
Results
The highest spore concentration (1 × 10^7 spores/mL) significantly
increased larval mortality, prolonged developmental stages, and reduced
reproductive success, particularly in pupation, adult emergence, and
female fecundity. Transcriptomic analysis revealed substantial
differences in gene expression between B. bassiana grown on artificial
media and during host infection at three-time points. At 48 hours’
post-infection, genes involved in adhesion and cuticle penetration,
such as serine/threonine-protein kinases (STPKs) and lipases, were
upregulated, indicating adaptation to host invasion. GO analysis
revealed enrichment in cellular and catalytic activities, while KEGG
pathways highlighted early-stage metabolic adaptations related to
nutrient acquisition and energy metabolism. In contrast, fungal growth
on artificial media showed minimal expression of infection-related
genes. At 96 hours, genes associated with ABC transporters and
detoxification were significantly upregulated, supporting fungal
survival and immune evasion. GO terms were enriched in membrane
components, and KEGG pathways focused on energy metabolism and stress
responses. At 144 hours, genes related to secondary metabolism were
upregulated, indicating the production of compounds vital for continued
invasion and immune suppression. The activation of these pathways were
minimal or absent during growth on artificial media.
Conclusions
This study provides new insights into the molecular adaptations of
B. bassiana during host infection, revealing key virulence factors and
infection dynamics. The identified gene expression signatures enhance
our understanding of fungal infection mechanisms and could inform more
effective biocontrol strategies for managing agricultural pests.
Keywords: Spodoptera frugiperda, Beauveria bassiana, Transcriptome,
Gene ontology (GO) enrichment analysis, Larval mortality
Introduction
The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith)
(Lepidoptera: Noctuidae), is a highly destructive agricultural pest
with a broad host range, causing severe economic losses in staple crops
such as maize, rice, sorghum, and wheat ([28]Diagne et al., 2021;
[29]Padhee & Prasanna, 2019; [30]Yang et al., 2020). Native to the
Americas, it has rapidly spread to Africa, Asia, and other regions,
posing a significant threat to global food security due to its strong
migratory ability, rapid reproduction, and resistance to conventional
chemical pesticides ([31]Goergen et al., 2016; [32]Kalleshwaraswamy et
al., 2018). A limitations of chemical control strategies, biological
control agents such as entomopathogenic fungi, including Beauveria
bassiana Bals. (Vuil.), have gained considerable attention as
sustainable alternatives for managing S. frugiperda populations
([33]Abdel Galil et al. 2019; [34]Montezano et al., 2018).
B. bassiana is a versatile entomopathogenic fungus capable of infecting
a broad range of insects, arachnids, and nematodes while also living
asymptomatically within plants as an endophyte ([35]Jensen et al.,
2020). Its virulence naturally varies across a host range of over 700
insect species, generating significant interest in studying
B. bassiana-host interactions ([36]Xiang et al., 2024). The infection
process involves critical stages, including initial adhesion, conidia
germination, appressorium formation, and cuticle degradation
facilitated by enzymes such as proteases and chitinases
([37]Ortiz-Urquiza, 2021; [38]Silva et al., 2020). Once fungus
proliferates inside the host as blastospores, evades immune detection,
releases toxins, and spreads through the hemolymph, ultimately leading
to host death ([39]Wang et al., 2017). The precise temporal and spatial
expression of numerous genes is crucial at various infection stages
([40]Qiu et al., 2015; [41]Valero-Jiménez et al., 2016). Fungal
pathogens must adapt to distinct environments to successfully infect
their hosts. B. bassiana undergoes significant physiological and
molecular changes when transitioning from saprophytic growth on
artificial media to a parasitic phase within a host insect. This
transition involves alterations in gene expression that are essential
for infection, survival, and reproduction. High-throughput methods are
frequently employed to identify genes involved in host-pathogen
interactions. While previous research identified over 2,000 genes under
different growth conditions via expressed sequence tag (EST) analysis
([42]Mantilla et al., 2012), a comprehensive understanding of global
gene expression in response to host environments remains limited
because of earlier methodological constraints. RNA-seq technology
offers a revolutionary approach for analyzing gene expression with high
resolution and detail, addressing previous limitations ([43]Guo et al.,
2024). The published genome of B. bassiana serves as a valuable
reference for transcriptome analyses to uncover genetic mechanisms
([44]Wang et al., 2017). Transcript profiling through RNA-seq has
proven effective in identifying gene sets that are differentially
expressed under specific conditions ([45]Stupnikov et al., 2021).
Recent transcriptomic studies of entomopathogenic fungi, including B.
bassiana, have revealed critical insights into gene regulation during
host infection. For instance, transcriptome analyses of Metarhizium
rileyi have identified genes involved in cuticle degradation ([46]Fan
et al., 2023). Similarly, comparative transcriptomic studies of
B. bassiana have identified differentially expressed genes during the
infection of various insect hosts, shedding light on
pathogenicity-related pathways, such as those regulating cuticle
penetration, stress response, and host adaptation ([47]Chen et al.,
2018a; [48]Mantilla et al., 2012). While these studies have provided
valuable insights into the gene expression of B. bassiana, they have
predominantly examined individual host species or specific growth
conditions. This narrow focus has resulted in a limited comparative
understanding of how B. bassiana’s gene expression differs between
infection of hosts and saprophytic growth.
This study aims to bridge this gap by employing RNA-seq transcriptomic
analysis to compare gene expression profiles at various time-interval
of B. bassiana infection of S. frugiperda versus growth on artificial
media. By identifying key genes and pathways involved in these
processes, we aim to enhance our understanding of how B. bassiana
adapts its biological functions to different environments.
Additionally, this study investigated the impact of various spore
concentrations on S. frugiperda larvae, assessing mortality,
developmental delays, and reproductive outcomes. This multifaceted
approach, which combines transcriptomic analysis and biological assays,
aims to uncover the molecular adaptations of B. bassiana during host
infection, contributing to our understanding of fungal pathogenicity
and laying a foundation for future studies on fungal biology and host
adaptation.
Materials & Methods
Insect rearing
The eggs of S. frugiperda used in this study were sourced from larvae
collected from maize fields in Guangdong Province, China, during the
maize growing season. In accordance with [49]Ge et al. (2022), the
collected larvae were cultured on a specially formulated artificial
diet. The diet consisted of the following components: wheat bran (50
g), soybean powder (40 g), maize powder (100 g), yeast powder (30 g),
agar (24 g), casein (40 g), sorbic acid (three g), ascorbic acid (3.5
g), vitamins (0.15 g), formaldehyde (four mL), glacial acetic acid
(four mL), and distilled water (1,200 mL). This mixture provided the
necessary nutrients for larval development and ensured consistent
growth conditions. The resulting adult S. frugiperda were provided with
artificial diets containing a 10% honey solution soaked in sterile
cotton balls. In the laboratory, the larvae were maintained under
controlled conditions at a temperature of 25 ± 2 °C, a photoperiod of
12:12 (dark: light), and a relative humidity of 65 ± 5%. After laying
their eggs, the batches were carefully transferred and placed in an
environmental growth chamber that maintained the laboratory conditions.
When they hatched, the newborn larvae were moved to transparent
rectangular plastic boxes measuring 28 × 17 × 18 cm.
Investigating the insecticidal potential of B. bassiana CDL1 against
third-instar S. frugiperda larvae
The entomopathogenic fungus B. bassiana CDL1 was cultivated on
1/4-strength Sabouraud dextrose agar with yeast extract (SDAY) medium
at a temperature of 25 °C for 14 days. Afterward, conidia were
collected from the fungus and suspended in a solution containing 0.01%
Tween-20; Tween-20 was added as a surfactant to reduce surface tension,
ensure even dispersion of the fungal spores, and prevent clumping,
thereby improving the uniformity of spore coverage during application
and ensuring accurate concentrations for reliable experimental results
to achieve a concentration of 3 × 10^8 conidia/mL. This initial
conidial suspension served as a stock mixture, and subsequent dilutions
were made to obtain concentrations of 1 × 10^4, 1 × 10^5, 1 × 10^6, and
1 × 10^7 spores/mL. For insecticidal activity, the third-instar larvae
of S. frugiperda were exposed to different concentrations of fungal
spores via a spray method. Each larva was treated with 0.5 mL of the
spore suspension applied uniformly using a handheld sprayer calibrated
to deliver a fine mist. The sprayer was held at a consistent distance
of 15 cm from the larvae to ensure even coverage, and the nozzle was
adjusted to produce droplets of approximately 50–100 µm in diameter.
This method ensured uniform spore distribution over the larval surface
and minimized variability in spore deposition. Control groups were
sprayed with sterile water containing 0.5% glycerin and 0.01% Tween-20.
Larval mortality was recorded daily, and dead larvae were surface
sterilized before being examined for the presence of fungal hyphae and
conidia to confirm their death ([50]Domingues et al., 2022). Parameters
such as larval duration, mortality rate, pupation rate, pupal duration,
pupal mortality, and adult deformities were recorded. Fecundity,
deficient fecundity percentage, and the oviposition deterrent index
were calculated for the emerging adults according to the following
equations:
A. Fecundity = Number of eggs laid per female
B. Deficient fecundity =
[MATH:
Control−TreatedControl×100
:MATH]
C. Oviposition deterrent index =
[MATH: C−TC+T×100 :MATH]
where C and T are the mean numbers of eggs laid in the control and
B. bassiana CDL1-treated larvae, respectively ([51]Huang, Renwick &
Chew, 1994). To analyze the mortality data, Abbott corrections were
applied, and probit analysis was conducted to determine the mean lethal
concentration (LC[50]) following established protocols ([52]Abbott,
1925; [53]Finney, 1971). This study included observations up to the
adult emergence stage, which offered a holistic perspective on the
insecticidal efficacy of B. bassiana CDL1.
Transcriptomic profiling of the entomopathogenic fungus B. bassiana strain
CDL1: investigation of pathogenicity and gene expression dynamics during
S. frugiperda infection
This study aimed to analyze the transcriptome of entomopathogenic
fungus B. bassiana strain CDL1, which was selected for its high
efficacy in causing larval mortality. The fungus was cultivated in 50
ml of 1/4-strength SDAY broth medium under controlled conditions:
shaking at 145 rpm/min, shaking at 24 °C, and a pH of 6.6. For
inoculation, a 0.5-mL culture containing approximately 1 × 10^8
conidia/mL was used. The choice of 1/4-strength SDAY broth media
allowed for the simulation of nutrient-limited conditions, which more
closely resemble the environment of fungus encounters during infection,
thus enhancing the expression of genes related to pathogenicity and
stress responses. Additionally, this nutrient limitation reduces the
overexpression of growth-related genes, ensuring a clearer focus on
infection-relevant processes ([54]Schumann, Smith & Wang, 2013;
[55]Zhang et al., 2021). To assess fungal pathogenicity, third-instar
S. frugiperda larvae were infected with the same spore suspension until
they reached the pupal stage. All samples were collected at 48, 96, and
144 hours’ post-infection to capture distinct transcriptomic profiles
across infection stages, with three biological replicates taken for
each time point, both from fungal culture in broth media and infected
larvae, individually. Immediately after collection, all samples were
snap-frozen in liquid nitrogen for five minutes to preserve RNA
integrity and stored at −80 °C until RNA extraction.
Total RNA extraction, library construction, and sequencing
Total RNA was extracted using the TRIzol reagent (Invitrogen, Carlsbad,
CA, USA), following the manufacturer’s instructions, with adaptations
for fungal growth in 1/4-strength SDAY broth medium and fungus-infected
larvae. For each condition and time point (48 h, 96 h, and 144 h),
three biological replicates were prepared to ensure statistical
reliability and capture biological variability. The quality of the RNA
was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies,
Palo Alto, CA, USA) and RNase-free agarose gel electrophoresis.
Eukaryotic mRNA was enriched using oligo(dT) beads, while prokaryotic
mRNA was enriched by removing rRNA with the Ribo-Zero™ Magnetic Kit
(Epicenter, Madison, WI, USA). The enriched mRNA was fragmented into
short pieces using fragmentation buffer and reverse transcribed into
cDNA using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB
#7530; New England Biolabs, Ipswich, MA, USA). The resulting
double-stranded cDNA fragments underwent end repair, A-base addition,
and ligation to Illumina sequencing adapters. The ligation reaction
mixture was purified using AMPure XP beads (1.0X) and subjected to
polymerase chain reaction (PCR) amplification. The final cDNA libraries
were sequenced on the Illumina NovaSeq 6000 platform. Each library was
sequenced to a depth ranging from 36.1 million to 49.5 million reads
per sample, ensuring sufficient coverage for accurate gene expression
and differential expression analysis. Sequencing was conducted by Gene
Denovo Biotechnology Co. (Guangzhou, China)
([56]https://www.genedenovo.com). Raw sequencing data underwent quality
control to remove low-quality reads, and the resulting clean data were
used for downstream analyses.
Mapping of RNA-seq reads and quantitative analysis of gene expression
The raw reads in FASTQ format were filtered via FASTP ([57]Chen et al.,
2018b) (version 0.18.0) to obtain clean data. Quality statistics,
including the Q20, Q30, and GC content, were calculated. The resulting
clean reads were then mapped to the reference genome of B. bassiana
([58]Xiao et al., 2012) via HISAT2 (v2.1.0) ([59]Kim et al., 2019). To
count the number of reads mapped to each gene, Gffcompare (version
0.12.6) ([60]Pertea & Pertea, 2020) was used, and the FPKM value was
calculated. The power analysis calculation (alpha = 0.05, effect size =
2) was carried out on all the genes of the triplicate cultures of
B. bassiana grown in 1/4-strength SDAY broth medium and fungus-infected
larvae, using the transcript per million (TPM) values as sequencing
depth. The analysis was conducted (online
at [61]https://rodrigo-arcoverde.shinyapps.io/rnaseq_power_calc/),
which confirmed sufficient statistical power for detecting
differentially expressed genes (DEGs).
The software DESeq2 ([62]Love, Huber & Anders, 2014) was used to
conduct differential expression analysis of RNAs between six different
groups. Genes with a false discovery rate (FDR) <0.05 and an absolute
fold change ≥ 2 were considered differentially expressed genes (DEGs).
GO functional classification and KEGG pathway enrichment analysis of DEGs
To further identify the functions of the differentially expressed
genes, the GO and KEGG enrichment analyses were done. For GO enrichment
analysis, the Gene Ontology database ([63]https://geneontology.org/)
was used. To ensure accuracy, all p-values were subjected to Bonferroni
correction ([64]Abdi, 2007) and a corrected p-value of <0.05 as the
threshold for significant enrichment of the gene sets were considered.
Additionally, the Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathway enrichment analysis for DEGs via the clusterProfiler package
([65]Yu et al., 2012) was performed.
RT-qPCR validation of differentially expressed genes identified via RNA-seq
To validate the differentially expressed genes (DEGs) identified
through RNA-seq analysis, we selected a total of 18 DEGs for validation
via real-time quantitative PCR (RT-qPCR). Initially, 10 DEGs were
randomly selected from the pool of statistically significant DEGs
(adjusted p-value < 0.05 and —log2FC— > 1) to provide a broad
validation of the RNA-seq results. Additionally, we included eight more
DEGs that were specifically chosen for their biological relevance and
involvement in key pathways or processes related to the study’s
objectives. This combined approach ensures both a representative
validation of the RNA-seq findings and a targeted analysis of
functionally important genes. RT-qPCR was performed using an iCycler iQ
Real-time PCR System (Bio-Rad, Hercules, CA, USA) with the QuantiNove
SYBR Green PCR Kit (Vazyme Biotech Co., Ltd., China), following the
manufacturer’s instructions. The cycling parameters were as follows:
initial denaturation at 95 °C for 10 s, followed by 40 cycles of 95 °C
for 10 s, 56.5 °C for 20 s, and 72 °C for 20 s. A melting curve
analysis was performed at the end of each run to confirm the
specificity of the amplified products. To normalize the expression
levels of the target DEGs, we used the expression of 18S rRNA as an
internal reference gene. Relative gene expression was calculated using
the 2^−ΔΔCt method. The primers used for RT-qPCR were designed using
Primer-BLAST (NCBI) and are listed in [66]Table S1. All reactions were
performed in triplicate to ensure technical reproducibility.
Statistical analysis
The bioassay experiments were conducted in triplicate to improve
reliability. The analyses included an independent t-test and one-way
ANOVA, followed by Duncan’s multiple range test. The statistical
program SPSS was used for these analyses. The results are presented as
the means ± SE, and a significance level of p < 0.05 was considered
statistically significant. Additionally, a heatmap illustrating the
concentration-dependent effects of B. bassiana CDL1 on various
parameters of S. frugiperda was generated using OriginLab version 2024.
Results
The impact of B. bassiana CDL1 spores on mortality rates and developmental
progression at the third larval instar of S. frugiperda
This study investigated the impact of different concentrations of
B. bassiana CDL1 spores on the mortality rates of S. frugiperda. The
results revealed that the mortality of both larvae and pupae was
positively correlated with increasing concentrations of B. bassiana
CDL1 spores. In particular, larval mortality increased from
19.99 ± 1.72% at the lowest concentration of 1 × 10^4 spores/mL to
66.66 ± 1.72% at the highest concentration of 1 × 10^7 spores/mL,
demonstrating the efficacy of the treatment. Pupal mortality also
increased, reaching 32.77 ± 1.94% at the highest concentration. The
transition from larvae to adults was significantly impeded, with a
failure rate of 99.44 ± 0.24% at the highest concentration. The
corrected percentage of individuals who failed to develop into adults
reached 95.05 ± 1.10% at the highest concentration when accounting for
natural mortality ([67]Fig. 1 and [68]Table 1). The results of the
probit analysis for the estimation of the LC[50] value for mortality in
S. frugiperda were 3.83 × 10^4 spores/mL ([69]Fig. S1). The LC[50]
values clearly revealed that B. bassiana had considerable toxic effects
on S. frugiperda.
Figure 1. Developmental stages of S. frugiperda infected by B. bassiana
strain CDL1: a morphological perspective.
[70]Figure 1
[71]Open in a new tab
(A) Normal larvae; (B) cadaver formation of larvae due to B. bassiana
(CDL1) infection; (C) normal pupae; (D) abnormal pupae with distinct
dark coloration infection; (E) normal adult; (F) deformed adult.
Table 1. Mortality rates of fall armyworm (S. frugiperda) at different
developmental stages after treatment with various concentrations of B.
bassiana CDL1 spores.
Conc. (spore/mL) Larval mortality (%) Pupal mortality (%) Individuals
failed to develop to adults (observed) (%) Individuals failed to
develop to adults (corrected) (%)
Control 8.88 ± 0.99[72]^a 0.00 ± 0.00[73]^a 8.88 ± 0.99[74]^a
0.00 ± 0.00[75]^a
1 × 10 ^4 19.99 ± 1.72[76]^ab 24.86 ± 1.99[77]^b 44.84 ± 2.18[78]^b
41.57 ± 1.37[79]^b
1 × 10 ^5 31.11 ± 2.62[80]^b 24.57 ± 2.02[81]^b 55.68 ± 3.95[82]^b
52.50 ± 3.87[83]^b
1 × 10 ^6 62.22 ± 0.99[84]^c 26.85 ± 1.49[85]^b 89.07 ± 1.07[86]^c
87.72 ± 1.15[87]^c
1 × 10 ^7 66.66 ± 1.72[88]^c 32.77 ± 1.94[89]^b 99.44 ± 0.24[90]^c
95.05 ± 1.10[91]^c
[92]Open in a new tab
Notes.
Data are presented as the mean value ± SE. Means in the same columns
followed by the same letters are not significantly different.
Effects of different concentrations of B. bassiana CDL1 spores on the
developmental parameters of S. frugiperda during the third larval instar
The findings presented in [93]Table 2 shed light on the influence of
spore concentration on crucial developmental stages. Notably, the
duration of larval development, measured in mean days, consistently
increased with corresponding concentrations of B. bassiana CDL1 spores.
The control group exhibited a development period of 10.03 ± 0.09 days,
which extended significantly to 14.86 ± 0.26 days at the highest spore
concentration. Moreover, the pupation rate decreased to 33.33 ± 1.72%
at the highest spore concentration, compared to the control group,
which had a pupation rate of 91.11 ± 0.94%. The pupal duration followed
a comparable trend, extending from 9.33 ± 0.14 days in the control
group to 14.33 ± 0.14 days at the highest spore concentration. In
parallel, adult emergence was severely affected, decreasing
dramatically from 91.11 ± 0.94% in the control group to just 0.56
± 0.14% at the highest concentration. Finally, adult longevity was
significantly reduced, with lifespan declining from 10.26 ± 0.24 days
in untreated individuals to only 1.33 ± 0.14 days in those exposed to
the highest concentration.
Table 2. Shows the effects of varying concentrations of B. bassiana CDL1
spores on different parameters of the fall armyworm, which was treated as the
third larval instar.
Conc. (spore/mL) Larval duration (Mean days ±) Pupation (%) Pupal
duration (Mean days ±) Adult emergence (%) Adult longevity (Mean
days ±)
Control 10.03 ± 0.09[94]^a 91.11 ± 0.94[95]^a 9.33 ± 0.14[96]^a
91.13 ± 0.99[97]^a 10.26 ± 0.24[98]^a
1 × 10 ^4 12.99 ± 0.08[99]^a 80.00 ± 1.72[100]^ab 9.66 ± 0.39[101]^ab
55.15 ± 2.81[102]^b 2.00 ± 0.25[103]^b
1 × 10 ^5 13.86 ± 0.31[104]^b 68.88 ± 2.62[105]^b 11.00 ± 0.25[106]^ab
44.31 ± 3.95[107]^b 1.66 ± 0.14[108]^b
1 × 10 ^6 14.39 ± 0.59[109]^b 37.78 ± 0.99[110]^c 11.66 ± 0.39[111]^b
10.93 ± 1.07[112]^c 1.33 ± 0.14[113]^b
1 × 10 ^7 14.86 ± 0.26[114]^b 33.33 ± 1.72[115]^c 14.33 ± 0.14[116]^c
0.56 ± 0.14[117]^c 1.33 ± 0.14[118]^b
[119]Open in a new tab
Notes.
Data are presented as the mean value ± SE. Means in the same columns
followed by the same letters are not significantly different.
The impact of B. bassiana CDL1 spores on the reproductive parameters of S.
frugiperda in the third larval instar
Different concentrations of B. bassiana CDL1 affect the reproductive
parameters of S. frugiperda in their third larval stage. The
concentration range varied from the control group to 1 × 10^7
spores/mL, revealing clear trends in the sex ratio, fecundity,
deficient fecundity, and oviposition deterrent index ([120]Table 3).
The sex ratio, representing the percentage of females in the
population, showed a steady increase with rising spore concentrations,
starting at 0.47 ± 0.12 in the control group and reaching 1.33 ± 0.14
at the highest concentration. Fecundity, measured as the number of eggs
laid per female, declined significantly as spore concentration
increased. The control group exhibited a high fecundity rate of 376.66
± 6.49 eggs per female. However, at a concentration of
1 × 10^6 spores/mL, egg production ceased entirely. This was reflected
by a deficient fecundity rate of 100.00 ± 0.00%, indicating a complete
inhibition of reproduction. The oviposition deterrent index, indicating
the effectiveness of B. bassiana CDL1 in deterring oviposition, tends
to increase with increasing spore concentration. The index reached
100.00 ± 0.00% at 1 × 10^6 spores/mL, indicating a complete deterrent
effect on oviposition at the highest concentrations tested.
Table 3. Illustrates the impact of varying concentrations of B. bassiana CDL1
spores on the fecundity and sex ratio of the fall armyworm, which was treated
as the third larval instar.
Conc. (spore/mL) Sex ratio (Female/total) No. of egg/female
(Fecundity) ± SE Deficient fecundity (%) Oviposition deterrent index
(%)
Control 0.47 ± 0.12[121]^a 376.66 ± 6.49[122]^a 0.00 ± 0.00[123]^a
0.00 ± 0.00[124]^a
1 × 10 ^4 0.44 ± 0.03^a 69.33 ± 2.84[125]^b 81.62 ± 0.61[126]^b
68.99 ± 0.86[127]^b
1 × 10 ^5 0.47 ± 0.03[128]^a 32.00 ± 1.86[129]^c 88.07 ± 1.37[130]^c
83.89 ± 0.98[131]^c
1 × 10 ^6 0.54 ± 0.04[132]^a 0.00 ± 0.00[133]^d 100.00 ± 0.00[134]^d
100.00 ± 0.00[135]^d
1 × 10 ^7 1.33 ± 0.14[136]^b 0.00 ± 0.00[137]^d 100.00 ± 0.00[138]^d
100.00 ± 0.00[139]^d
[140]Open in a new tab
Notes.
Data are presented as the mean value ± SE. Means in the same columns
followed by the same letters are not significantly different.
Impact of concentration-dependent B. bassiana on life stages of S.
frugiperda: a heatmap analysis
This study provides a detailed examination of the effects of B.
bassiana CDL1 on various life stages of S. frugiperda through a
concentration-dependent heatmap analysis. The heatmap in [141]Fig. 2
visually represents the impacts of different spore concentrations of B.
bassiana CDL1 on key biological parameters, including larval and pupal
mortality, developmental duration, fecundity, and sex ratio. The
numbers within the heatmap circles indicate the percentage of larvae
and pupae that died at each concentration, showing that higher
concentrations of B. bassiana resulted in increased mortality,
emphasizing its potential efficacy as a biocontrol agent. Additionally,
the heatmap reveals that elevated spores concentrations extended the
duration of the larval and pupal stages, indicating a disruption of
normal development and may inducing physiological stress caused by
infection. Reductions in fecundity were observed as spore
concentrations increased, with fewer eggs laid by adult females, while
the sex ratio data show a shift in the proportion of males to females,
implying potential alterations in population structure. The color
gradients of the heatmap highlight the intensity of these effects, with
darker shades corresponding to greater impacts, providing a
comprehensive visual overview of the multifaceted interactions between
B. bassiana CDL1 and various life cycle parameters of S. frugiperda.
This analysis demonstrates both lethal and sublethal effects of fungal
infection on developmental and reproductive outcomes.
Figure 2. Heatmap analysis reveals concentration-dependent effects of B.
bassiana CDL1 on various parameters of the fall armyworm (S. frugiperda).
[142]Figure 2
[143]Open in a new tab
Effects of B. bassiana CDL1 on S. frugiperda at different time points
The virulence of the B. bassiana CDL1 strain was tested on S.
frugiperda larvae via a highly concentrated suspension of 1 × 10^8
spores/mL. As shown in [144]Fig. 3, the spray resulted in mortality
rates of approximately 8.3 ± 1.52%, 53.3 ± 4.16%, and 69.66 ± 2.50% at
48, 96, and 144 h after B. bassiana infection (BbI), respectively.
Figure 3. The mortality rates of S. frugiperda larvae infected with spore
suspensions (1 × 10^8 spores/mL) and water.
[145]Figure 3
[146]Open in a new tab
The error bars represent the standard error of the mean from three
replicates.
Overview of sequencing data
To gain a comprehensive understanding of B. bassiana CDL1, we conducted
genome-wide transcriptome analysis via RNA-seq during its growth on
1/4-strength SDAY broth media and infection of S. frugiperda larvae at
various time points. During fungal growth on 1/4-strength SDAY broth
media, the total number of reads ranged from 36,164,664 to 49,431,470.
The mapping rates and unique mapping percentages were consistently
high, ranging between 90.30% and 91.52%. In contrast, when B. bassiana
CDL1 infected S. frugiperda larvae, each treatment sample at different
time points yielded varying numbers of raw reads. At 48 h post
infection (hpi), the number of raw reads ranged from 39,080,418 to
40,853,568. At 96 hpi, the range was between 37,188,274 and 41,593,374
raw reads. At 144 hpi, the number of raw reads ranged from 44,245,422
to 47,535,454. At 48 and 96 hpi, only 1.30 to 1.90% of the reads
uniquely mapped to the reference genome, indicating that B. bassiana
CDL1 was present in lower quantities during the early stages of
infection. However, at 144 hpi, 2.0 to 4.42% of the reads uniquely
mapped to the reference genome, suggesting significant proliferation of
the fungus at this stage. This increase in fungal-mapped reads
indicates a higher fungal transcript abundance relative to earlier time
points, reflecting active fungal growth and gene expression within the
host. The elevated proportion of fungal reads suggests that the
pathogen has successfully breached host defenses and is proliferating
extensively, likely due to established colonization and tissue invasion
at this stage of infection. These findings highlight the complexity and
challenges of mapping reads in host–pathogen interactions. These
findings also suggest potential variability in fungal gene expression,
exhibiting significant changes during infection compared with that
during growth in 1/4-strength SDAY broth media ([147]Table 4).
Table 4. Summary of RNA-Seq data and mapping.
Sample Total read number Unmapped (%) Unique mapped (%) Multiple mapped
(%) Total mapped (%)
F-48 h-a 3,6164,664 9.36 90.38 0.26 90.64
F-48 h-b 3,8962,380 9.92 90.30 0.28 90.58
F-48 h-c 3,7013,128 9.32 90.41 0.27 90.68
F-96 h-a 4,1215,256 9.19 90.55 0.26 90.81
F-96 h-b 3,9500,932 9.26 90.49 0.24 90.74
F-96 h-c 3,9047,758 9.31 90.45 0.25 90.69
F-144 h-a 4,9431,470 8.78 90.87 0.35 91.22
F-144 h-b 3,8615,438 8.22 91.47 0.31 91.78
F-144 h-c 3,9765,996 8.20 91.52 0.28 91.80
L-48 h-a 4,0853,568 98.27 1.70 0.03 1.73
L-48 h-b 3,9080,418 98.31 1.67 0.02 1.69
L-48 h-c 3,9574,240 98.69 1.30 0.01 1.31
L-96 h -a 3,7188,274 98.18 1.80 0.02 1.82
L-96 h-b 4,1253,392 98.09 1.90 0.01 1.91
L-96 h-c 4,1593,374 98.11 1.87 0.02 1.89
L-144 h-a 4,4245,424 95.57 4.40 0.03 4.43
L-144 h-b 4,7535,454 97.95 2.01 0.04 2.05
L-144 h-c 4,4245,422 95.55 4.42 0.03 4.45
[148]Open in a new tab
Notes.
F represents fungal growth in 1/4-strength SDAY broth medium, whereas L
refers to larvae that are infected by the fungus; a, b, and c are three
biological replicates.
A comprehensive global analysis of the genes expressed during the growth of
B. bassiana on artificial media and infection of S. frugiperda
Sequencing data from each sample were mapped to the B. bassiana genome,
revealing that up to 5,225, 10,861, and 6,458 fungal genes were
expressed in differential expression gene (DEG) libraries from B.
bassiana growing on 1/4-strength SDAY broth media and infected larvae
at 48, 96, and 144 h, respectively. These findings indicate that a
significant number of genes in B. bassiana are activated during
critical infection stages. For this study, differentially expressed
genes (DEGs) were defined as those with a false discovery rate (FDR)
< 0.05 and an absolute fold change ≥ 2. On the basis of these criteria,
we identified 4,589, 5,839, and 6,458 DEGs between fungal growth on
1/4-strength SDAY broth media and infected larvae at 48, 96, and 144 h,
respectively. Compared with those in larvae growing on 1/4-strength
SDAY broth media, the numbers of upregulated genes in infected larvae
were 930, 1,284, and 1,145 at 48, 96, and 144 h, respectively, whereas
the numbers of downregulated genes were 3,659, 4,555, and 5,313 at
these time points, respectively ([149]Fig. 4). These findings suggest
that each stage of infection influences distinct biological processes
in the fungus.
Figure 4. Volcano plots and differential gene expression (DEG) analysis of B.
bassiana CDL1 during fall armyworm infection compared to growth on artificial
medium.
[150]Figure 4
[151]Open in a new tab
(A) 48 h, (B) 96 h, (C) 144 h: Volcano plots showing the DEGs
identified during infection compared to fungal growth on artificial
medium. (D) Total number of DEGs at each time point (‘L’ = larvae
infected by fungus, ‘F’ = fungus growth on artificial medium).
Enrichment of GO terms and KEGG pathway analysis
The GO annotation analysis of DEGs during S. frugiperda by B. bassiana,
compared with that of the fungus growing on a 1/4-strength SDAY broth
media, revealed significant enrichment in the following GO terms across
all three GO domains: cellular process, metabolic process, binding,
catalytic activity, and membrane, highlighting their critical roles in
infection dynamics ([152]Fig. 5). Among the top 20 significantly
enriched GO terms, “catalytic activity” and “intrinsic component of
membrane” were notably represented ([153]Fig. 6). The increase in
catalytic activity indicates the high involvement of DEGs in
enzyme-mediated biochemical reactions, which are essential for the
fungus to adapt to the host environment, acquire nutrients, and
counteract host defenses. The enrichment of the intrinsic component of
the membrane suggested that many DEGs are involved in membrane-related
functions, such as transport, signaling, and maintaining cell
integrity, which are crucial for pathogen entry into host cells,
evasion of host immune responses, and intercellular communication
during infection. To gain deeper insights into the functional roles of
differentially expressed genes (DEGs) in B. bassiana during the
infection of S. frugiperda and its growth on 1/4-strength SDAY broth
media, KEGG pathway enrichment analysis was performed. This analysis
categorized the biological functions of the DEGs by mapping them to
terms in the KEGG database. Specifically, significant enrichment was
detected in 25 pathways at 48 h post-infection, 18 pathways at 96 h,
and 19 pathways at 144 hours’ post-infection. The top 20 pathways,
ranked by the gene ratio, are displayed as the ratio of the number of
DEGs to the total number of genes in a given pathway ([154]Fig. 7).
Notably, at 48 hours’ post-infection, metabolic pathways were among the
top pathways. At 96 h, both metabolic pathways and ABC transporters
were prominent. After 144 h, metabolic pathways and the biosynthesis of
secondary metabolites were the top pathways. These results provide
valuable insights into the temporal dynamics of gene expression and the
key biological pathways involved in the infection process.
Figure 5. Differential Gene Ontology (GO) analysis to compare the B. bassiana
in fall armyworm larvae and growth on artificial media at different time
periods.
[155]Figure 5
[156]Open in a new tab
(A) The 48-hour timeframe; (B) the 96-hour timeframe; (C) the 144-hour
timeframe.
Figure 6. Top 20 GO terms with significant enrichment.
[157]Figure 6
[158]Open in a new tab
(A–C) The top 20 Gene Ontology (GO) terms with significant enrichment
when the responses to fungal infection in larvae versus fungal growth
on artificial media at different timeframes were compared. (A) The
48-hour timeframe, (B) the 96-hour timeframe, and (C) the 144-hour
timeframe.
Figure 7. KEGG pathways for differentially expressed genes (DEGs) comparing
infection in larvae and fungal growth on artificial medium.
[159]Figure 7
[160]Open in a new tab
(A) The 48-hour timeframe; (B) the 96-hour timeframe; (C) the 144-hour
timeframe. The gene ratio (rich factor) is the ratio of the number of
DEGs to the total number of genes in a given pathway, indicating the
level of enrichment.
Comparative temporal gene expression profiling of B. bassiana during
infection of S. frugiperda larvae and growth on artificial media across
different time points
Several genes exhibit significant upregulation in the infected
condition (BbI) compared to growth on artificial media (BbM) at the
48-hour time point. Notably, the lipase gene shows a 10.46-fold
increase in BbI, while the siderophore iron transporter gene mirB is
upregulated by 11.48-fold in BbI. These findings suggest potential
roles in lipid degradation and iron acquisition, both of which may
contribute to fungal pathogenesis. By 96 h, the expression of the
lipase gene decreases substantially, and mirB expression is also
reduced, indicating a shift in metabolic processes as the infection
progresses. In contrast, genes such as metalloprotease-like protein,
cytochrome P450 3A9, and glutathione S-transferase omega-1 are
significantly upregulated at later time points (96 and 144 h),
suggesting an increased reliance on proteolysis, detoxification, and
secondary metabolism as the infection advances. Additionally, genes
involved in stress response, such as ribonuclease and V-type ATPase,
exhibit fluctuating expression profiles across the time points,
reflecting the fungus’s adaptation to host immune responses and
environmental stresses. At 96 and 144 h, chitinase III and
endochitinase III, which are associated with cell wall degradation,
show upregulation, potentially facilitating the penetration of host
tissues.
This dataset provides a comprehensive view of the temporal dynamics in
gene expression of B. bassiana during host infection and growth on
artificial medium, highlighting key metabolic and adaptive processes
underlying fungal pathogenesis ([161]Table 5 and [162]Fig. 8).
Table 5. Temporal gene expression profiling of B. bassiana during fall
armyworm larval infection compared with that during growth on artificial
media at different time points.
Gene name or description Mean expression in BbI (48 h) (FPKM) Mean
expression in BbM (48 h) (FPKM) log [2] Fold Change (48 h) Mean
expression in BbI (96 h) (FPKM) Mean expression in BbM (96 h) (FPKM)
log [2] Fold Change (96 h) Mean expression in BbI (144 h) (FPKM) Mean
expression in BbM (144 h) (FPKM) log [2] Fold Change (144 h)
Lipase 332.66 0.23 10.46 0.001 0.33 −8.06 0.001 0.71 −8.48
ATP synthase subunit J 1,762.16 442.22 2.00 606.62 288.44 1.07 166.94
343.37 −1.35
Siderophore iron transporter mirB 573.24 0.20 11.48 0.001 0.78 −9.61
0.001 4.30 −12.07
Sugar transporter STL1 923.17 14.86 5.95 48.30 38.20 0.34 22.58 23.70
−0.06
Putative RING finger protein 15.07 10.64 5.66 70.20 82.58 −0.23 22.58
23.70 −0.06
Extracellular serine-rich protein 1.45 12.13 −3.15 16.53 8.21 1.00
41.16 2.03 4.35
Serine/threonine- protein kinase rio2 684.93 39.71 4.10 20.40 25.27
−0.31 0.001 22.54 −14.46
Chitinase III 1.27 7.75 −2.60 27.32 3.81 2.84 20.30 11.20 0.86
Endochitinase III 0.001 3.97 −11.95 24.33 4.17 2.54 51.22 41.88 0.29
metalloprotease-like protein 3.32 31.56 −3.24 73.70 19.23 1.93 115.36
28.46 2.01
Pyroglutamyl- peptidase 1 10.40 11.55 −0.26 0.001 15.20 −13.84 103.21
23.69 2.12
Cytochrome P450 3A9 4.70 3.28 0.51 0.001 2.84 −11.47 22.91 5.04 2.16
putative methyltransferase C20orf7 44.87 16.06 1.48 0.001 11.70 −13.51
106.64 23.04 2.21
Glutathione S-transferase omega-1 0.57 14.87 −4.70 0.001 11.49 −13.48
79.17 7.84 3.33
beta-lactamase/ transpeptidase- like protein 0.89 3.07 −1.77 0.001 3.25
−11.66 19.21 2.75 2.80
ubiquinol- cytochrome C reductase 10.60 37.56 −1.82 0.001 23.91 −14.54
299.54 26.03 3.52
Ribonuclease 0.001 29.63 −14.85 0.001 39.15 −15.25 97.06 16.44 2.56
Fructosamine -3-kinase 7.19 187.47 −4.70 N/A N/A N/A 200.03 22.92 3.12
aldo/keto reductase 725.13 197.91 1.87 N/A N/A N/A 105.44 7.59 3.79
V-type ATPase 0.001 0.14 −7.16 15.60 1.07 3.86 0.001 0.758 −9.56
Catalase-1 3.36 3.03 0.14 N/A N/A N/A 92.50 4.80 4.26
Choline-sulfatase 19.41 13.63 0.50 67.01 10.14 2.72 0.001 16.53 −14.01
Putative sucrose utilization protein SUC1 779.71 54.56 3.83 N/A N/A N/A
291.91 189.71 0.62
Alkylated DNA repair protein alkB 8 26.41 33.62 −0.34 0.001 15.27
−13.84 66.16 11.14 2.56
Putative acyl-CoA synthetase YngI 73.54 0.001 16.16 N/A N/A N/A 0.001
0.07 −6.29
Putative metal ion transporter C17A12.14 589.31 49.60 3.56 N/A N/A N/A
57.29 71.56 −0.32
ppic-type ppiase domain containing protein 48.73 22.97 1.08 0.001 21.31
−14.37 216.66 24.64 3.13
Sexual differentiation process protein isp4 7.84 7.91 −0.01 0.001 7.58
−12.88 56.21 11.17 2.33
[163]Open in a new tab
Notes.
The log[2] ratio (BbI/BbM) represents the fold change in gene
expression between two conditions: BbI (B. bassiana-infected larvae)
and BbM (B. bassiana growth on 1/4-strength SDAY broth medium). N/A
indicates data for genes were not detected for the given time point or
condition. The FPKM method is employed to eliminate the influence of
different gene lengths and sequencing discrepancies on the calculation
of gene expression.
Figure 8. Temporal dynamics of virulence related gene expression in infection
of B. bassiana to S. frugiperda.
[164]Figure 8
[165]Open in a new tab
Validation of differentially expressed genes via RT-qPCR
To validate the differentially expressed genes (DEGs) identified
through RNA-Seq analysis, we conducted RT-qPCR on 18 selected genes,
comprising 10 randomly chosen DEGs and eight genes specifically
associated with B. bassiana pathogenicity against S. frugiperda. The
eight targeted genes—lipase (LIP), metalloprotease-like protein
(MGG-80), siderophore iron transporter mirB (MFS2), sugar transporter
STL1 (STL1), putative RING finger protein (SPCF3.16), cytochrome P450
3A9 (FUM15), serine/threonine-protein kinase rio2 (rio2), and chitinase
III (chi2)—were selected based on the following criteria: (1) They were
among the most significantly differentially expressed; (2) they
represent key pathways involved in pathogenicity, such as cuticle
degradation, nutrient acquisition, stress responses, and
detoxification; and (3) they include both up- and down-regulated genes
across different time points of infection (48 h, 96 h, and 144 h). The
RT-qPCR results confirmed the differential expression patterns observed
in the RNA-Seq data for all 18 genes ([166]Fig. 9). Among the eight
targeted genes, significant upregulation was observed at early stages
of infection (48 h) for genes such as lipase, siderophore iron
transporter mirB, and sugar transporter STL1, which are involved in
cuticle degradation and nutrient acquisition. In contrast, genes like
metalloprotease-like protein and chitinase III exhibited dynamic
expression patterns, with upregulation at later stages (96 h and 144
h), suggesting their roles in host tissue degradation and nutrient
acquisition during advanced infection. Genes associated with stress
responses (putative RING finger protein, serine/threonine-protein
kinase rio2) and detoxification (cytochrome P450 3A9) showed variable
expression across time points, reflecting their potential roles in
adapting to host defenses and environmental stress. The correlation
between RNA-seq and RT-qPCR results was strong across all time points,
confirming the reliability of the RNA-seq data ([167]Fig. 9). These
findings validate the differential expression of genes involved in key
pathogenic pathways and provide further insights into the temporal
regulation of B. bassiana’s infection process in S. frugiperda.
Figure 9. Validation of differentially expressed genes (DEGs) by RT-qPCR.
[168]Figure 9
[169]Open in a new tab
(A–C) Expression profiles of eight targeted genes associated with B.
bassiana pathogenicity against S. frugiperda across different time
points (48 h, 96 h and 144 h). (D) Expression profiles of 10 randomly
selected DEGs. Error bars in all panels (A–D) represent mean ± SE from
three biological replicates.
Discussion
In this study, we investigated the effects of B. bassiana CDL1 on S.
frugiperda and compared the gene expression profiles of the fungus
during infection with those observed under standard growth conditions.
Our analysis provides insight into how B. bassiana alters its gene
expression in response to its insect host and highlights the
differential expression of genes involved in pathogenicity and the
stress response.
The mortality of the larvae observed in our study significantly
influenced overall mortality rates and exhibited a similar trend. We
found that the percentage of larval mortality was positively correlated
with total mortality, and this correlation became stronger as the
concentration of B. bassiana spores increased. These findings were
supported by [170]Nelly, Reflinaldon & Meriqorina (2023), who reported
a positive correlation between B. bassiana concentration and S.
frugiperda larval mortality. Specifically, they reported that a B.
bassiana suspension with a concentration of 1 × 10^9 conidia/mL
resulted in the highest mortality rate of 84%. These findings suggest
that the effectiveness of B. bassiana in infecting S. frugiperda larvae
increases with increasing conidia density. The death of larvae caused
by entomopathogenic fungi is a result of the production of toxic
metabolites, including beauvericin, beauverolite, bassianalite,
bassianolide, and isorolite ([171]Abdullah, Abd El-Wahab & Abd
El-Salam, 2024; [172]Bi et al., 2023; [173]San Juan-Maldonado et al.,
2024; [174]Vishaka et al., 2020). These compounds destroy the digestive
system, muscles, nervous system, and respiratory system of insects; the
synergy of all these toxins causes the death of the preyed-upon insect
([175]Pedrini, 2022). These findings are consistent with earlier
research on other entomopathogenic fungi, further underscoring the
potential of B. bassiana as an effective biocontrol agent.
On the other hand, our results demonstrate that the latent effect of B.
bassiana CDL1 spores during the larval stage can extend to the pupal
and adult stages. This is evidenced by the observed pupal mortality in
S. frugiperda insects treated with spores. These findings are
consistent with those of [176]Islam et al. (2023), who reported that
the EPF isolate TGS2.3 had a sublethal effect on different life stages
of Spodoptera litura. Specifically, fungal infection during the larval
stage directly results in pupal mortality. The entomopathogenic fungus
B. bassiana reportedly interferes with insect molting, preventing the
larvae from successfully progressing into the pupal stage
([177]Torrado-León, Montoya-Lerma & Valencia-Pizo, 2006). The molting
process is fundamental to the production of new cuticles, but it can be
disrupted by fungal impact. Since the formation of these cuticles
relies on nutritional resources, any imbalance in hemolymph nutrients
caused by fungal infection can negatively impact the molting process at
different stages ([178]Islam et al., 2023). The findings of the present
study suggest that the sex ratio of adult S. frugiperda is
significantly biased toward females due to the treated larvae. This
indicates that male individuals within the population are more
susceptible to B. bassiana spores than females. In their study,
[179]Korany et al. (2019) reported that, compared with female
chitinase, crude chitinase had a greater effect on the growth of male
individuals during their developmental stages. Several factors can
contribute to sex differences in disease susceptibility, such as
variations in body mass and the immune response. Generally, males are
more susceptible to diseases, whereas females often have stronger
immune responses. This disparity in immune strength may explain the
increased incidence of autoimmune diseases and malignancies among
females ([180]Kecko et al., 2017). The dominance of emerging adult
females after treatment was clear. However, their fecundity study
revealed no eggs at a relatively high concentration of 1 × 10^6
spores/mL. The concentration-dependent reduction in fecundity aligns
with the findings of [181]Kaur et al. (2014). These authors also
reported significant reductions in S. litura fecundity, adult
emergence, longevity, and hatching percentages when S. litura was
exposed to relatively high concentrations of secondary metabolites from
Streptomyces hydrogenans DH16. Furthermore, they observed morphological
abnormalities compared with those in the control groups. The ability of
B. bassiana to reduce S. frugiperda female fecundity and survival is
attributed to physiological alterations resulting from pathogenic
infection ([182]Jin et al., 2015; [183]Usman et al., 2021). For
example, entomopathogenic fungi (EPFs), such as B. bassiana, can
deplete sugar and other compounds in insect hemolymph ([184]Jin et al.,
2015; [185]Peng et al., 2015; [186]Xia, Clarkson & Charnley, 2002),
significantly affecting host insect fitness parameters ([187]Jin et
al., 2015). The decrease in oviposition rate and fertility among
infected females is closely linked to EPF action, which negatively
impacts insect populations and compensates for their delayed mortality
([188]Dimbi, Maniania & Ekesi, 2013). Furthermore, the antifeedant
activity of B. bassiana during the invasive process may contribute to
the observed reduction in fecundity ([189]Ekesi, 2001). These sublethal
effects are critical for long-term pest suppression, as they disrupt
the reproductive potential of surviving populations.
Our transcriptomic analysis of B. bassiana CDL1 during its interaction
with S. frugiperda reveals significant changes in gene expression
compared to its growth in artificial media. Over time, the increase in
uniquely aligned reads indicates a rise in fungal proliferation within
the host. These findings align with the results of [190]Chen et al.
(2018a), who observed a similar increase in aligned reads over time
during the infection of B. bassiana in Galleria mellonella. Similarly,
[191]Zhou et al. (2019) reported an increase in read mapping during the
infection of B. bassiana against both G. mellonella and Plutella
xylostella, further supporting the observed trend of enhanced fungal
activity during host infection.
Temporal gene expression profiling identified specific genes with
significant expression changes during infection compared to growth on
artificial media across different time points. In the early stages of
infection, adhesion to the host cuticle and subsequent penetration are
critical for the successful invasion of B. bassiana CDL1. Understanding
the differential expression of these genes provides valuable insights
into the infection process. For instance, serine/threonine-protein
kinases (STPKs) play a pivotal role in host recognition and the
activation of downstream signaling pathways, highlighting their
importance in fungal pathogenesis. Specifically, the rapid upregulation
of STPK gene expression at 48 hours’ post-infection (BbI), followed by
downregulation at 96 h and sustained low levels thereafter, suggests
their involvement in the formation of infection structures. This
observation is consistent with the findings of [192]Gormal et al.
(2024), who demonstrated that STPKs are activated upon detecting
host-specific receptors or molecules, triggering signal transduction
pathways such as mitogen-activated protein kinase (MAPK) and protein
kinase A (PKA). During cuticle penetration, B. bassiana upregulates
genes encoding lipases and chitinases, which target the epicuticular
lipids and chitin layers ([193]Gao et al., 2011; [194]Zhang et al.,
2012). Notably, at 48 hours’ post-infection (BbI), we observed a marked
increase in lipase expression, suggesting its key role in degrading
host lipids to facilitate penetration. Comparing these findings with
studies on other fungal entomopathogens, such as Metarhizium and
Isaria, reveals both similarities and differences in their gene
expression profiles. For instance, Metarhizium species show an
upregulation of lipases during the early stages of infection,
emphasizing the role of lipid degradation in penetrating the host
cuticle and evading immune defenses ([195]Gao et al., 2011;
[196]Reingold et al., 2024). In contrast, Isaria species may regulate
proteolytic enzymes differently during these early stages, reflecting
distinct strategies for overcoming host immune responses. Similarly,
the upregulation of chitinases is noteworthy, as these enzymes are
essential for degrading the chitin matrix, further facilitating
successful cuticle penetration. Our results align with those of
[197]Lai et al. (2017), who found that chitinases were highly expressed
at 60 h post-infection (hpi), promoting the digestion of mosquito
cuticle chitin and enabling penetration of the procuticle. This
underscores the pivotal role of these enzymes in the initial
interaction between fungal pathogens and their insect hosts. Our
results also show high expression of the catalase gene after 144 h of
infection, likely reflecting a response to host-derived oxidative
stress, as reactive oxygen species (ROS) such as hydrogen peroxide
(H[2]O[2]) are produced to combat pathogens. Catalase, an antioxidant
enzyme, breaks down H[2]O[2], protecting the pathogen from oxidative
damage. This delayed upregulation suggests a strategic adaptation to
establish infection and counteract sustained oxidative stress during
later stages. Similar findings were reported by [198]Wang et al.
(2013b), who demonstrated increased catalase expression in prolonged
host-pathogen interactions, highlighting its role in pathogen survival
under oxidative stress conditions. Furthermore, our data reveal that
the RING finger protein exhibits increased expression at 48 hpi,
suggesting its involvement in protein degradation, signaling, and
fungal adaptation. This aligns with previous studies by [199]Zhang et
al. (2010), who reported similar roles for RING finger proteins in
fungal pathogens, highlighting their importance in modulating host
interactions and stress responses. Similarly, the upregulation of the
sugar transporter STL1 in our study underscores B. bassiana’s enhanced
ability to exploit host sugars, which is consistent with the findings
of [200]Wang et al. (2013a). These authors demonstrated that STL1
facilitates fungal proliferation by optimizing nutrient acquisition
from the host, further supporting our observations. Additionally, the
increased expression of the siderophore iron transporter (mirB) at 48
hpi in our study suggests a critical role in iron acquisition, which is
essential for fungal virulence. This finding corroborates earlier work
by [201]Wang et al. (2017), who identified mirB as a key player in iron
scavenging, a process vital for pathogen survival and host
colonization. The upregulation of metalloprotease-like genes at 144
hours’ post-infection (hpi) suggests their potential role in
suppressing the host immune system, possibly by inactivating
prophenoloxidase, a key component of insect immunity. This observation
aligns with the findings of [202]Huang et al. (2020), who highlighted
the crucial function of metalloproteases in evading host defenses.
Likewise, the increased expression of cytochrome P450 3A9 (CYP3A9) at
144 hpi indicates its involvement in detoxification processes. This is
consistent with studies by [203]Črešnar & Petrič (2011) and
[204]Forlani et al. (2014), which identified cytochrome P450 as
essential for fungal survival, particularly in detoxifying xenobiotics,
breaking down harmful compounds, and synthesizing secondary metabolites
vital for pathogenicity.
Conclusions
This study highlights the virulence of B. bassiana CDL1 against S.
frugiperda, demonstrating a concentration-dependent effect on
mortality, development, and reproduction. Higher spore concentrations
significantly increased mortality rates, delayed developmental
progression, and reduced fecundity, culminating in complete
reproductive suppression at the highest concentrations. The pathogenic
potential of B. bassiana CDL1 was evident through its ability to
disrupt multiple life stages of the insect.
Transcriptome analysis revealed distinct gene expression patterns
during fungal infection and artificial growth, identifying key pathways
associated with virulence, metabolism, and host adaptation. These
findings underscore B. bassiana CDL1 as a highly promising biocontrol
agent, exhibiting strong pathogenicity against S. frugiperda.
Supplemental Information
Supplemental Information 1. List of primers used for RT-qPCR validation
of differentially expressed genes.
[205]peerj-13-19591-s001.docx^ (20.2KB, docx)
DOI: 10.7717/peerj.19591/supp-1
Supplemental Information 2. Toxicity profile of B. bassiana CDL1
against S. frugiperda treated in the third larval instar.
[206]peerj-13-19591-s002.jpg^ (77.1KB, jpg)
DOI: 10.7717/peerj.19591/supp-2
Supplemental Information 3. B. bassiana raw data fall armyworm data for
[207]Tables 1–[208]3.
[209]peerj-13-19591-s003.xlsx^ (35KB, xlsx)
DOI: 10.7717/peerj.19591/supp-3
Supplemental Information 4. B bassiana raw data heatmap life stages S.
frugiperda for [210]Fig. 2.
[211]peerj-13-19591-s004.xlsx^ (13.3KB, xlsx)
DOI: 10.7717/peerj.19591/supp-4
Supplemental Information 5. B. bassiana raw data mortality comparison
S. frugiperda for [212]Fig. 3.
[213]peerj-13-19591-s005.xlsx^ (9.2KB, xlsx)
DOI: 10.7717/peerj.19591/supp-5
Supplemental Information 6. B. bassiana raw data transcriptomic
validation for [214]Fig. 9.
[215]peerj-13-19591-s006.xlsx^ (9.7KB, xlsx)
DOI: 10.7717/peerj.19591/supp-6
Supplemental Information 7. Data: RT-qPCR primers and LC[50]
estimation.
[216]peerj-13-19591-s007.docx^ (47KB, docx)
DOI: 10.7717/peerj.19591/supp-7
Funding Statement
This study was supported by the National Key R&D Program of China
(2022YFD1401001) and the Science and Technology Innovation Project of
the Qinling Institute in NWAFU (2452023301). The funders had no role in
study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Additional Information and Declarations
Competing Interests
The authors declare there are no competing interests.
Author Contributions
Hamdy H. Aly performed the experiments, analyzed the data, prepared
figures and/or tables, authored or reviewed drafts of the article, and
approved the final draft.
Yun Meng performed the experiments, authored or reviewed drafts of the
article, and approved the final draft.
Dun Wang conceived and designed the experiments, authored or reviewed
drafts of the article, and approved the final draft.
DNA Deposition
The following information was supplied regarding the deposition of DNA
sequences:
The raw sequence reads are available at GenBank: [217]PRJNA1106630.
Data Availability
The following information was supplied regarding data availability:
The raw data is available in the [218]Supplemental Files.
References